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Senior Honors Theses

Senior Honors Theses

Skin cancer: causes, prevention, and treatment.

Lauren Queen Follow

Publication Date

Spring 4-10-2017

School of Health Sciences

Health Promotion: Clinical Track

Skin Cancer, Melanoma, Prevention, Treatment, Dermatology


Diseases | Medicine and Health Sciences | Skin and Connective Tissue Diseases

Recommended Citation

Queen, Lauren, "Skin Cancer: Causes, Prevention, and Treatment" (2017). Senior Honors Theses . 648.

The purpose of this thesis is to analyze the causes, prevention, and treatment of skin cancer. Skin cancers are defined as either malignant or benign cells that typically arise from excessive exposure to UV radiation. Arguably, skin cancer is a type of cancer that can most easily be prevented; prevention of skin cancer is relatively simple, but often ignored. An important aspect in discussing the epidemiology of skin cancer is understanding the treatments that are available, as well as the prevention methods that can be implemented in every day practice. It is estimated that one in five Americans will develop skin cancer during his or her lifetime, and that one person will die from melanoma every hour of the day. To an epidemiologist and health promotion advocate, these figures are daunting for a disease, especially for a disease that has ample means of prevention. However, even with sufficient prevention methods, a lack of education and promotion of a practice will not lead to favorable results. This thesis will aim to uncover the causes and treatments associated with skin cancer, the disease, distribution, and determinants of the disease, and finally, how the promotion of the practice of prevention of this disease can be furthered.

Since April 12, 2017

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Skin cancer detection using deep learning—a review.

skin cancer thesis statement

1. Introduction

2. convolutional neural networks (cnns) for image classification, 2.1. commonly used cnn architectures for image classification, 2.2. alexnet, 2.4. resnet, 2.5. densenet, 2.6. mobilenet, 3. deep-learning-based classification of skin cancers, 4. types of skin cancer and commonly used datasets for skin cancers, 4.1. type of skin cancer, 4.1.1. melanoma, 4.1.2. dysplastic nevi, 4.1.3. basal cell carcinoma (bcc), 4.1.4. squamous cell carcinoma (scc), 4.1.5. actinic keratoses (aks), 4.2. datasets, 4.2.1. ham10000, 4.2.2. p h 2, 4.2.3. isic, 4.2.4. isic2016, 4.2.5. isic 2017, 4.2.6. isic2018, 4.2.7. isic 2019, 4.2.8. isic 2020, 4.2.9. atlas of dermoscopy, 4.2.10. dermofit, 4.2.11. bcn20000, 4.2.12. pad-ufes-20, 5. resources required for training proposed dl algorithms, 6. conclusions and discussion, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Pacheco and Krohling [ ]2019Reviewed deep learning models for skin cancer classification
Lucieri et al. [ ]2021Reviewed deep-learning-based decision support in skin cancer diagnosis
Adegun and Viriri [ ]2021Reviewed deep learning techniques for skin lesion analysis and melanoma cancer detection
Dildar et al. [ ]2021Reviewed deep learning algorithms for skin cancer classification
Gilani and Marques [ ]2023Reviewed skin lesion classification and segmentation using generative adversarial networks (GANs)
Krizhevsky et al. [ ]AlexNet2012
Simonyan and Zisserman [ ]VGG2015
He et al. [ ]ResNet2016
Huang et al. [ ]DenseNet2017
Howard et al. [ ]MobileNet2017
Inthiyaz et al. [ ]Xiangya-DermCNNAUC = 0.87
Gajera et al. [ ]ISIC 2016, ISIC 2017, , HAM10000AlexNet, VGG-16, VGG-19,Accuracy = 98.33%,
F1 score = 0.96
Alenezi et al. [ ]ISIC 2017, HAM10000deep residual networkAccuracy = 96.971%,
F1-score = 0.95
Shinde et al. [ ]ISICSqueeze-MNetAccuracy = 99.36%
Alenezi et al. [ ]ISIC 2019, ISIC 2020ResNet-101 with SVMAccuracy = 96.15% (ISIC 2019), 97.15% (ISIC 2020)
Abbas and Gul [ ]ISIC 2020NASNetAccuracy = 97.7%,
F1-score = 0.97
Gouda et al. [ ]ISIC 2018CNNAccuracy = 83.2%
Alwakid et al. [ ]HAM10000CNN, ResNet-50F1-score = 0.859 (CNN), 0.852 (ResNet-50)%
Bassel et al. [ ]ISICResnet50, Xception, and VGG16Accuracy = 90.9%,
F1-score = 0.89
Kousis et al. [ ]ISIC 2019Eleven CNN architectures with DensNet169 giving the best resultsAccuracy = 92.25%,
F1-score = 0.932
Shorfuzzaman [ ]ISIC archiveDenseNet121, Xception, EfficientNet80Accuracy = 95.76%,
F1-score = 0.957
Reis et al. [ ]HAM10000 (ISIC 2018), ISIC 2019, ISIC 2020InSiNet, U-NetAccuracy = 94.59% (ISIC 2018), 91.89% (ISIC 2019), and 90.54% (ISIC 2020)
Fraiwan and Faouri [ ]HAM10000thirteen CNN architectures with DensNet201 giving the best resultAccuracy = 82.9%,
F1-score = 0.744
Ghosh et al. [ ]HAM10000, ISIC archiveSkinNet-16Accuracy = 95.51% (HAM10000), 99.19% (ISIC)
Maniraj and Maran [ ] VGG-based hybrid architectureAccuracy = 93.33%
Alam et al. [ ]HAM10000S2C-DeLeNetMean Accuracy = 91.03%, Mean Dice = 0.9494
Mazoure et al. [ ]ISICInceptionv313, ResNet5014, 170 MobileNetv23, EfcientNet15, BYOL16, SwAVClass prediction probability = 1.00 (Mel)
Malibari et al. [ ]ISIC 2019DNNAverage accuracy = 99.90%, F1-score = 0.990
Rashid et al. [ ]ISIC 2020MobileNetV2-based transfer learningAverage accuracy = 98.20%
Aljohani and Turki [ ]ISIC 2019DenseNet201, MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16, VGG19, and GoogleNetAccuracy = 76.09%
Bian et al. [ ]ISBI 2016YoDyCKAccuracy = 96.2%
Demir et al. [ ]ISIC archiveResNet-101, Inception-v3F1-score = 84.09% (ResNet-101), 87.42% (Inception-v3)
Jain et al. [ ]HAM10000Transfer learning-based VGG19, InceptionV3, InceptionResNetV2, ResNet50, Xception, and MobileNetAccuracy = 90.48% (Xception)
Bechelli and Delhommelle [ ]Kaggle dataset, HAM10000CNN, pre-trained VGG-16, Xception, ResNet50Accuracy = 88% (VGG-16),
F1-score = 0.88 (VGG-16)
Khan et al. [ ]Segmentation (ISBI 2016, ISBI 2017, ISIC 2018, ), classification (HAM10000)ResNet101, DenseNet201Accuracy = 98.70% (Segmentation, ), Accuracy = 98.70% (Classification)
Adegun et al. [ ]ISBI 2017, fully convolutional neural networkAccuracy = 97% (ISBI 2016)
Qasim Gilani et al. [ ]HAM10000Spiking VGG-13Accuracy = 89.57%,
F1-score = 0.9007
Lu and Firoozeh Abolhasani Zadeh [ ]HAM10000XceptionAccuracy = 100%,
F1-score = 95.55%
Khan et al. [ ]ISBI 2016, ISIC 2017, ISBI 2018, ISIC 2019, HAM10000A hybrid framework of 20 layered and 17 layered CNN for segmentation, 30 layered CNN for feature extractionSegmentation Accuracy = 92.70% (ISIC 2018), Classification Accuracy = 87.02% (HAM10000)
Abdar et al. [ ]ISIC 2019 [ ]ResNet152V2, MobileNetV2, DenseNet20Best Accuracy = 89% [ ], F1-score = 0.91 [ ]
Inthiyaz et al. [ ]Used pre-trained model for feature extraction and classification was performed using softmax classifier.This work was tested on a very small dataset; these results can not be generalized on large datasets.  Inthiyaz et al. [ ] achieved an AUC of 0.87, which can still be improved; citeinthiyaz2023skin used a deep architecture ResNet-50 which increases the computational cost.
Gajera et al. [ ]Used eight pre-trained CNN models for the classification of dermoscopy images.The proposed methods were evaluated on , ISIC 2016, and ISIC 2017 with only 200, 900, and 2000 training images. Using deep architectures such as DenseNet-121 on small datasets may result in overfitting. Classification performance on the HAM10000 dataset was low.
Alenezi et al. [ ]Used wavelet-transform-based deep residual neural network for the classification of skin cancer images.Limited generalizability. Weak classification performance on lesion images having different sizes, colors, etc.
Shinde et al. [ ]Lightweight model was proposed for the classification of skin cancer images on IOT devices.The proposed model in this work had lower sensitivity and specificity than other baseline models. Since this model was proposed for the classification task on IOT, it should have fewer training parameters than other baseline methods, such as MobileNetV2. However, the number of parameters and training time was still greater than MobileNetV2.
Alenezi et al. [ ]Multi-stage deep model was used for the extraction of features from skin cancer images.Dataset 1 only contained 1168 images. Deep architectures such as ResNet-101 were used for feature extraction, which may result in overfitting. Features extracted using deep networks were provided to SVM for the classification of skin cancer images; it has limitations in terms of the time required for the parameter selection of the SVM classifier.
Abbas and Gul [ ]Proposed architecture for the classification of skin cancers.Proposed a NASNet for the classification of skin cancer images that extracts generalizable features.
Gouda et al. [ ]Pre-trained models were used for the classification of skin cancer images. ESRGAN was used for augmenting the dataset.The proposed work was tested on a small dataset using 3533 images from ISIC 2018. The best classification accuracy of 0.8576 was obtained using Inception50, which is still low. The accuracy achieved using this method was low compared to dermoscopy.
Alwakid et al. [ ]Data augmentation and segmentation of lesion was used to improve the classification performance.Used ESRGAN for data augmentation. Moreover, performed the segmentation to segment lesions for accurate classification. Proposed CNN-based architecture for the classification. The proposed work achieved an accuracy of 86%, less than the dermoscopy images’ accuracy.
Bassel et al. [ ]Pre-trained models were used for extracting features. Stacked CV techniques consisting of five different classifiers were used for the classification of skin cancer images.The proposed model was trained and tested on a small dataset of 2637 training images and 660 test images. The proposed stacked CV algorithm gave the best classification accuracy of 90.9% on the features extracted using Xception. The model may not perform well on large datasets as it will have limited generalizability because a very small dataset was used for training.
Kousis et al. [ ]Evaluated the performance of eleven CNN on the skin cancer classification task, and created a mobile application using the best model.Among the eleven architectures used in this work, DenseNet 169 gave the best classification accuracy of 92.5%. Deploying DenseNet169 for skin cancer classification is not computationally efficient.
Shorfuzzaman [ ]Explainable CNN-based stacked framework was proposed for the classification of skin melanoma images.The proposed work combined deep models such as DenseNet 121, Xception, and EfficientNetB0 to classify skin cancer images. A total of 3297 images from ISIC 2018 were used for training, and an accuracy of 95.76% was achieved using the proposed method. The proposed method is tested only for melanoma versus non-melanoma problems. The proposed model needs to be tested on large datasets, and combining three deep models will be computationally expensive.
Reis et al. [ ]Deep CNN network, InSiNet, was proposed for the classification of skin cancer images.Very deep models trained on only 1323 images were used for classifying melanoma and non-melanoma images. The proposed model can not be generalized to a large dataset as it is trained on 1323 images only.
Fraiwan and Faouri [ ]Evaluated thirteen transfer learning models for the classification of skin cancers.DenseNet201 gave the best accuracy of 82.9% and an F1-score of 0.744. F1-score is more suited for performance evaluation as HAM10000 is an imbalanced dataset; the F1-score of 0.7424 achieved in this work was quite low. Precision and recall, which are also important metrics in skin diagnosis, were quite low.
Ghosh et al. [ ]Proposed SkiNet-16, a CNN for the classification of skin cancers. PCA was used for feature selection.Used two different datasets for the evaluation of the proposed method; dataset 1 consists of only 3297 images, and dataset 2 consists of 1954 images. The method was tested for melanoma versus non-melanoma cases. Skin cancer images were classified with very high accuracy.
Maniraj and Maran [ ]Multi-stage hybrid deep learning modeling employing 3D wavelets were proposed.The proposed mode was tested on only 200 images and can not be used to aid skin cancer diagnosis. The proposed model’s performance will degrade when trained and tested on large datasets.
Alam et al. [ ]Proposed SC-DeLeNet for the segmentation and classification of skin cancer images.The proposed S2C-DeLeNet1 was implemented in two stages; in the first stage, Efficient-Net B4 was used as the encoder of U-Net for the segmentation, and the encoder–decoder network was used for features extraction in stage 2. S2C-DeLeNet1 tested on the HAM10000 dataset consisting of 10,000 images from seven classes performed well on both tasks.
Mazoure et al. [ ]CNN-based webserver was developed for the detection of skin cancers.Among six deep learning networks trained in this work, ResNet-50 gave the best class prediction probability of 1.00. The web server was developed only for benign versus malignant cases.
Malibari et al. [ ]CNN-based optimal method for detecting and classifying skin cancer images.The proposed mode was trained on ISIC 2019 consisting of 253,331 performed well on all five metrics, accuracy, F1-score, precision, recall, and specificity, and gave an impressive accuracy 99.99%.
Rashid et al. [ ]MobileNetV2 based transfer learning framework was proposed for skin cancer classification problem.Addressed the problem of the class imbalance problem. The proposed model performed well on all four metrics used in this study, accuracy, recall, F1 score, and precision, and achieved an average accuracy of 98.2%. The model was tested on only the binary classification problem.
Aljohani and Turki [ ]Evaluated seven different deep learning models on skin cancer classification problem.The models were evaluated on the dataset comprising 7146 images from two classes. The best accuracy of 76.08% achieved using GoogleNet on the test set was quite low.
Bian et al. [ ]YoDyCK: YOLOv3 optimized by dynamic convolution kernel trained on skin cancer images collected from was proposed. WGAN was used for data augmentation.Addressed the problem of data bias in the skin lesion dataset by training the proposed model on the images collected from Asian countries.
Demir et al. [ ]Classified skin cancer images using Inception-v3 and ResNet-101.Inception-v3 trained on 2437 images from two classes gave the best F1 score of 87.02%.
Jain et al. [ ]Used different transfer learning models for feature extraction and classification of skin cancers.Xception gave the best accuracy, but the computation time was greater than other networks trained in this study. The accuracy was MobileNet was a bit low than Xception, but it required less time for training.
Bechelli and Delhommelle [ ]Performance of different machine learning and deep learning algorithms was evaluated on skin cancer datasets.Obtained better accuracy and F1 score on smaller datasets. Deep learning models trained on the HAM10000 dataset achieved an F1-score of 0.70 and a precision of 0.68, which were low when tested on a smaller dataset.
Khan et al. [ ]Proposed CNN-based fully automated method for the classification and segmentation of images.The classification accuracy of the proposed model trained on the HAM10000 was high, but the proposed model gave the best segmentation performance on , which has only 200 images; the effectiveness of the proposed method should be evaluated by testing it on larger datasets.
Adegun et al. [ ]Improved the fully connected convolutional network segmentation using probabilistic model.The proposed model trained using fewer parameters achieved a good classification accuracy on ISBI data, but it required more time to train.
Lu and Firoozeh Abolhasani Zadeh [ ]Improved XceptionNet for the classification of skin cancer images.The proposed model achieved the 100% accuracy and F1-score of 95.3% on HAM10000 dataset. Precision and sensitivity were also greatly improved as compared to other networks.
Qasim Gilani et al. [ ]Used spiking neural network (SNN) for the classification of skin cancer images.SNN trained using fewer parameters achieved higher accuracy, and F1-score than the deep learning models but the specificity and precision of VGG-13 was higher. SNN used in this work is preferred for hardware implementation because of its power-efficient behavior.
Khan et al. [ ]Developed an automated system for collecting and uploading skin lesion images on the cloud and performing classification and segmentation.Information fusion and improved segmentation methods used in this work improved the performance. However, the use of information fusion increased the feature dimensionality, resulting in increased computational cost.
Abdar et al. [ ]A hybrid deep learning model for the classification of skin cancer images.The proposed work assessed the performance of uncertainty quantification methods, Monte Carlo (MC) dropout, ensemble MC dropout (EMC), and deep ensemble (DE) and selected the best-performing models for skin cancer diagnosis.
PaperResources Required for Training Deep Learning Algorithms Covered in This Paper
Gajera et al. [ ]Intel Core i7-7700 (8) CPU @ 4.20 GHz and 16 GB RAM with a single NVIDIA GeForce GTX 1050Ti GPU
Alenezi et al. [ ]32 GB RAM and an NVIDIA Quadro P4000 card
Shinde et al. [ ]Intel Core i5-7500 3.40 GHz processor, 32 GB of RAM, NVIDIA GeForce GTX 10050Ti graphical processor Raspberry Pi 4 microprocessor with a 64-Gb SD card, spy camera, and NeoPixel ring
Alenezi et al. [ ]Intel Xeon processor, 64 GB of RAM, and 8 GB-P4000 GPU.
Abbas and Gul [ ]12 GB GPU and 25 GB of RAM.
Gouda et al. [ ]Linux PC with GPU RTX3060 and 8 GB of RAM.
Alwakid et al. [ ]Linux PC with RTX3060 and 8 GB of RAM.
Bassel et al. [ ]Core Intel4 processor with 12 GB RAM.
Kousis et al. [ ]Linux system with a GTX 1060 6 GB graphics card.
Shorfuzzaman [ ]NVIDIA Tesla P100 GPU with 16 GB RAM
Reis et al. [ ]Intel i5 processor, 6 GB of RAM, and a GTX 940MX NVidia GPU with 2 GB of VRAM
Fraiwan and Faouri [ ]HP OMEN 30L desktop GT13 with 64 GB RAM, an NVIDIA GeForce RTX 3080 GPU, an Intel Core i7-10700K CPU @ 3.80 GHz, and a 1TB SSD.
Alam et al. [ ]Ryzen 5600 CPU and Nvidia RTX3060Ti GPU (8 GB VRAM).
Mazoure et al. [ ]NVIDIA P40 GPU with 4 CPUs.
Malibari et al. [ ]i5–8600k, GeForce 1050Ti 4 GB, 16 GB RAM, 250 GB SSD, and 1 TB HDD
Bian et al. [ ]i7-8700k CPU and two 1080ti GPUs
Khan et al. [ ]16 GB RAM and 256 GB SSD, 16-GB graphics card
Lu and Firoozeh Abolhasani Zadeh [ ]Intel Core™ i7-4720HQ, CPU 1.60 GHz, RAM 16 GB Frequency 1.99 GHz,
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Naqvi, M.; Gilani, S.Q.; Syed, T.; Marques, O.; Kim, H.-C. Skin Cancer Detection Using Deep Learning—A Review. Diagnostics 2023 , 13 , 1911.

Naqvi M, Gilani SQ, Syed T, Marques O, Kim H-C. Skin Cancer Detection Using Deep Learning—A Review. Diagnostics . 2023; 13(11):1911.

Naqvi, Maryam, Syed Qasim Gilani, Tehreem Syed, Oge Marques, and Hee-Cheol Kim. 2023. "Skin Cancer Detection Using Deep Learning—A Review" Diagnostics 13, no. 11: 1911.

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Skin Cancer—The Importance of Prevention

In 2009, the US Preventive Services Task Force (USPSTF) found insufficient evidence to recommend skin examinations for the early detection of skin cancer in adults. The conclusion followed from a systematic review of the effectiveness and harms of clinical visual skin examinations by physicians or patient self-examinations in terms of morbidity and mortality from skin cancer.

Several years later, after another systematic review, 1 the USPSTF’s conclusion—that there is insufficient evidence to recommend total-body skin examination for the early detection of melanoma, basal cell cancer, or squamous cell cancer in all adults—remains the same. 2

The USPSTF’s determination that evidence is not adequate to support a recommendation for skin cancer screening will likely once again disappoint national organizations such as the American Academy of Dermatology and the Skin Cancer Foundation, which have advocated for screening. 3 , 4 Physicians and patients might also be confused. After all, several organizations have encouraged screening; skin cancer seems easy to detect early because it is visible; skin examinations are neither painful nor invasive; and melanoma thickness at the time of diagnosis predicts mortality.

However, the USPSTF recommendations are based on a rigorous evidence review that balanced the benefits and risks of screening. The potential benefits are apparent but the risks, such as unnecessary procedures and their downstream complications, may not be. Over treatment of skin cancer may be especially problematic for patients with limited life expectancy due to old age or comorbidities. These patients may not live long enough to benefit from more intensive treatments but may be at risk for short-termtreatment-relatedcomplications. 5

The USPSTF review identified no completed randomized clinical trials on the topic. The USPSTF rightly focused on the initially exciting results of an ecologic study, Skin Cancer Research to Provide Evidence for Effectiveness of Screening in Northern Germany (SCREEN), conducted in 1 German state during 2003–2004. 6 The SCREEN study showed a 48% relative reduction in melanoma mortality in the state by 2009 after initiation of a population-based skin cancer awareness campaign, clinician education and training, and screening of nearly 20% of eligible adults aged 20 years and older with a single clinical visual skin examination. Those results prompted Germany to institute a nationwide program of clinical visual skin examinations. Unfortunately, the mortality benefit was not sustained with further follow-up, and several major methodological concerns about SCREEN have been raised. 7 , 8

Skin Cancer Is a Major Problem

The incidence of skin cancer is higher than that of all other cancers combined. Both melanoma and nonmelanoma skin cancer incidence rates continue to increase. The 5.4 million new cases of basal and squamous cell carcinomas in the United States annually 9 and 76 380 new cases of malignant melanoma each year 10 raise concerns for both patients and the health care system. Skin cancer treatments cost the United States more than $8 billion each year, making skin cancer the fifth most costly cancer for Medicare. Furthermore, skin cancer is an under recognized problem for diverse populations, including young women and minorities such as Hispanic individuals and gay men.

If universal screening is not the right approach, what can we do? The answer is that we can do a lot, if we shift our focus from secondary prevention (catching a cancer early enough to treat it) to primary prevention (preventing the cancer from developing in the first place). More than half of cancers are considered preventable through behavioral changes, vaccinations, or medications. 11 The evidence suggests that much of skin cancer could also be prevented.

Preventability of Skin Cancer

The UV radiation from indoor tanning beds is a group 1 carcinogen, in the same category as tobacco or asbestos. 12 Preventing carcinogenic exposures can result in preventing cancer. Indoor tanning is estimated to cause more than 450000 new skin cancers, including more than 10000 melanomas, each year. 13 Despite substantial investment in prevention efforts, including several well- designed campaigns by the Centers for Disease Control and Prevention and foundations focused on skin cancer prevention, efforts to affect the incidence of skin cancer have hit a brick wall. Tanning bed use remains common, with 1 in 5 adolescents and more than 40% of college students using tanning beds. 13

What are we doing wrong? In part, we might not be using the right tools to reach teens and young adults directly, and we might not be reaching the mat the right time. That is where technology may help. Social media and online search engines provide the ability to target health messages directly to those at highest risk. These platforms provide away to introduce messages precisely when teens are, for example, searching for a tanning salon. 14 Technology that targets health messages can get the right message to the right person at the right time. Refining messages that can shift social norms about tanning in general and studying whether these can actually change behaviors remain priorities.

Established and effective strategies for skin cancer prevention are also underused. Comprehensive sun-protection programs that emphasize shade and sun-protective clothing such as Australia’s SunSmart program ( slip on clothing, slop on sunscreen, slap on a hat, seek shade, and slide on sunglasses) should be implemented widely. The Australian program has been linked to a decrease in the incidence of skin cancer in young adults. 15 , 16 Strategies that go beyond education and address practical, environmental, and behavioral barriers to sustainable sun protection have the highest likelihood of success. Shade structures in playgrounds and free sunscreen dispensers in outdoor parks are innovative ideas that should be evaluated. In addition, there are lessons from successful antismoking efforts. Based on the experience with smoking cessation programs, increasing the legal age for indoor tanning to 21 years, restricting indoor tanning advertising directed to youths, and increasing taxation for indoor tanning beyond the 10% excise tax imposed by the Patient Protection and Affordable Care Act may be effective approaches. Physicians and the public should remain alert to the indoor tanning industry’s use of the same techniques used by the tobacco industry: paying scientists to bring doubt to the evidence, making false advertising claims about the health benefits of tanning, and undermining the scientific consensus on the adverse health effects of indoor tanning.

Does Skin Cancer Screening Make Sense for High-Risk Individuals?

As new data emerge, we might find that the benefits of skin cancer screening outweigh the risks for high-risk individuals. Such individuals include solid-organ transplant recipients who have 3 times higher risk of developing malignant melanoma and more than 60 times higher risk of cutaneous squamous cell carcinoma. They also include people with a history of multiple skin cancers whose probability of developing another skin cancer is 50% within 1 year and 70% within 3 years of their last skin cancer diagnosis as well as people with a strong family history of melanoma. As more is learned about the genetic predictors of melanoma and other skin cancers, genotypic approaches may be developed to stratify and identify individuals at high risk who could benefit from screening.


The USPSTF recommendations should not be misinterpreted as minimizing the importance of skin cancer. Instead, the report should motivate us to improve the evidence base for identifying groups of people in whom the benefits of screening might outweigh risks. We need high-quality, long-term randomized clinical trials of the effectiveness of screening on skin cancer prevention. Meanwhile, we should also fully implement skin cancer primary prevention by eliminating indoor tanning exposure, especially among youths, and increasing the use of sun-protection strategies that work.

Conflict of Interest Disclosures: None reported.

59 Skin Cancer Essay Topic Ideas & Examples

🏆 best skin cancer topic ideas & essay examples, 📌 good essay topics on skin cancer, 🔎 simple & easy skin cancer essay titles.

  • Life Quality Concerns After a Melanoma Diagnosis Melanoma is one of the most dangerous forms of skin cancer and has been on the rise over the past 30 years.
  • Skin Cancer: Description, Causes, and Treatment Skin cancer is one of the most common types of cancer; the three most common types of skin cancer are basal cell carcinoma, squamous cell carcinoma, and melanoma.
  • Researching of Cause and Effects of Melanoma This essay reviews the causes of melanoma, including the genetic aberrations involved, and discusses some of the effects of this cancer.
  • Does the Sun Radiation Cause Skin Cancer? Moreover, from the article written by American Cancer Society, it is evident that Ultraviolet A and Ultraviolet B from the sun lead to skin cancer.
  • Malignant Melanoma of the Skin It is better to quit smoking and choose a healthy diet with the help of which he could improve his immune system.
  • Malignant Melanoma of the Skin Diagnostics and Screening Are the examinations of your body organized by your wife systematic or occasional? If systematic, what are the reasons for them?
  • Indoor Tanning as a Cause of Melanoma Sarah Longwell’s claim that there is no scientific evidence to confirm that indoor tanning is one of the leading causes of melanoma is invalid.
  • Malignant Melanoma: Diagnosis and Treatment But when the above process is taking place, the pro-inflammatory signals engaged in skin healing and repair produce the twin effect of not only arousing the cells of the immune system but also enhancing “…the […]
  • Melanoma: Risk Factors and Treatment One of the most problematic is melanoma it is a cancer of the skin. Melanoma is a preventable disease but ignorance of the problem is the reason why this medical condition has claimed the lives […]
  • Skin Cancer: Comparison of Samples The aim of this experiment is to examine and thereafter represent low and high power illustrations of a normal skin specimen and of skin specimens that have been affected by various forms types of skin […]
  • Skin Cancer and Sunlight: Case Control, Cohort, and Clinical Trial Design The main component in sunlight that is said to be responsible for the development of skin cancer is the Ultraviolet emission.
  • Skin Cancer Awareness Overview Other causes of skin cancer include; family history of skin cancer, personal history of the disease, over exposure to the sun, history of sunburn early in life, large moles, freckles and light skin complexity. The […]
  • Malignant Melanoma: Pathology and Epidemiology Melanoma is the most rapidly growing type of cancer in the world, as well as the fifth leading cancer in men and the seventh in women in America.
  • Skin Cancer: Diagnosis and Treatment In order to prevent the incidence of skin cancer, the patients are recommended to undergo regular cancer screenings. Thus, following the suggested recommendations is expected to reduce the incidence of skin cancer among patients.
  • Skin Cancer in Australia and Health Campaign According to the International Agency for Research on Cancer, the incidence of skin cancer in Australia is the highest in the world.
  • Skin Cancer: Types and Cells of Origin Skin cancer is very often considered a disease connected with the cell cycle, although it is not actually the case, as cancer cells can easily grow and divide.
  • The Problem of Skin Cancer in Australia Generally the issue of skin cancer in Australia is widely known to people despite the fact that the cases have not been mitigated appropriately.
  • Aggressive Malignant Melanoma Skin Cancer
  • Dangers, Symptoms and Treatment of Skin Cancer
  • Methods of Battle With Skin Cancer
  • Description of Breast Cancer and Skin Cancer
  • Benefits of Green Tea for Skin: Acne, Skin Cancer, and Others
  • Natural Products for Treatment of Skin Cancer
  • The Connection Between Cutaneous Papillomaviruses and Non-melanoma Skin Cancer
  • Biomarkers of Response for Checkpoint Inhibitor Therapy in Skin Cancer
  • Dangers, Symptoms, and Treatment of Skin Cancer, a Malignant Disease
  • Deadly Skin Cancer Form Malignant Melanoma
  • The Efficacy and Safety of Sunscreen Use for the Prevention of Skin Cancer
  • Human Papillomaviruses and Polyomaviruses in Skin Cancer
  • Insights Into Biomarkers, Cytokines, and Chemokines in Skin Cancer
  • Fluorescence-Guided Pdt for Optimization of the Outcome of Skin Cancer Treatment
  • Direct Sunlight vs. Tanning Beds in the Development of Skin Cancer Malignant Melanoma
  • The Connection Between Genetic Damage and Skin Cancer
  • Genetic Mutations That Cause Skin Cancer
  • Genetic Risk Factors and Offsetting Behavior: The Case of Skin Cancer
  • Skin Cancer Awareness and Prevention
  • Indoor Tanning and the Perils of Skin Cancer
  • Melanoma: The Most Dangerous Forms of All Skin Cancer
  • Muffin Technique Micrographic Surgery for Non-melanoma Skin Cancer
  • Non-melanoma Skin Cancer Measurement
  • The Ins and Outs of Chemokine-Mediated Immune Cell Trafficking in Skin Cancer
  • The Statistics and Prevalence of Skin Cancer World Wide
  • Adapting a Skin Cancer Prevention Intervention for Multiethnic Adolescents
  • Tumor-Associated Macrophages: Therapeutic Targets for Skin Cancer
  • Skin Cancer Prevalence and Geographic Location
  • How Caffeine, Exercise Help Fight Skin Cancer
  • Skin Cancer: Types, Symptoms, Risk Factors & Treatment
  • The Global Health Problem of Skin Cancer
  • An Effective Skin Cancer Prevention Strategy
  • Risk of Skin Cancer in Tanning Beds
  • Detecting the Symptoms of Skin Cancer
  • Stratospheric Ozone Depletion and Its Effect on Skin Cancer Incidence
  • Skin Cancer: The Potential Hazards of Too Much Sun
  • Most Effective Way to Cure Skin Cancer
  • Skin Cancer as a Major Public Health Problem
  • Ultraviolet Light Is the Main Cause of Skin Cancer
  • Skin Cancer: The Dangers of Wanting a Dark Tan
  • Chronic Pain Research Ideas
  • Health Insurance Research Topics
  • Leukemia Topics
  • Medical Marijuana Topics
  • Palliative Care Research Topics
  • Pathogenesis Research Ideas
  • Pharmacy Research Ideas
  • Healthcare Reform Essay Ideas
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IvyPanda. (2023, September 27). 59 Skin Cancer Essay Topic Ideas & Examples.

"59 Skin Cancer Essay Topic Ideas & Examples." IvyPanda , 27 Sept. 2023,

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IvyPanda . 2023. "59 Skin Cancer Essay Topic Ideas & Examples." September 27, 2023.

1. IvyPanda . "59 Skin Cancer Essay Topic Ideas & Examples." September 27, 2023.


IvyPanda . "59 Skin Cancer Essay Topic Ideas & Examples." September 27, 2023.


Home > Eppley Institute > Theses & Dissertations

Theses & Dissertations: Cancer Research

Theses/dissertations from 2024 2024.

Novel Spirocyclic Dimer (SpiD3) Displays Potent Preclinical Effects in Hematological Malignancies , Alexandria Eiken

Dying Right: Supporting Anti-Cancer Therapy Through Immunogenic Cell Death , Elizabeth Schmitz

Therapeutic Effects of BET Protein Inhibition in B-cell Malignancies and Beyond , Audrey L. Smith

Identifying the Molecular Determinants of Lung Metastatic Adaptation in Prostate Cancer , Grace M. Waldron

Identification of Mitotic Phosphatases and Cyclin K as Novel Molecular Targets in Pancreatic Cancer , Yi Xiao

Theses/Dissertations from 2023 2023

Development of Combination Therapy Strategies to Treat Cancer Using Dihydroorotate Dehydrogenase Inhibitors , Nicholas Mullen

Overcoming Resistance Mechanisms to CDK4/6 Inhibitor Treatment Using CDK6-Selective PROTAC , Sarah Truong

Theses/Dissertations from 2022 2022

Omics Analysis in Cancer and Development , Emalie J. Clement

Investigating the Role of Splenic Macrophages in Pancreatic Cancer , Daisy V. Gonzalez

Polymeric Chloroquine in Metastatic Pancreatic Cancer Therapy , Rubayat Islam Khan

Evaluating Targets and Therapeutics for the Treatment of Pancreatic Cancer , Shelby M. Knoche

Characterization of 1,1-Diarylethylene FOXM1 Inhibitors Against High-Grade Serous Ovarian Carcinoma Cells , Cassie Liu

Novel Mechanisms of Protein Kinase C α Regulation and Function , Xinyue Li

SOX2 Dosage Governs Tumor Cell Identity and Proliferation , Ethan P. Metz

Post-Transcriptional Control of the Epithelial-to-Mesenchymal Transition (EMT) in Ras-Driven Colorectal Cancers , Chaitra Rao

Use of Machine Learning Algorithms and Highly Multiplexed Immunohistochemistry to Perform In-Depth Characterization of Primary Pancreatic Tumors and Metastatic Sites , Krysten Vance

Characterization of Metastatic Cutaneous Squamous Cell Carcinoma in the Immunosuppressed Patient , Megan E. Wackel

Visceral adipose tissue remodeling in pancreatic ductal adenocarcinoma cachexia: the role of activin A signaling , Pauline Xu

Phos-Tag-Based Screens Identify Novel Therapeutic Targets in Ovarian Cancer and Pancreatic Cancer , Renya Zeng

Theses/Dissertations from 2021 2021

Functional Characterization of Cancer-Associated DNA Polymerase ε Variants , Stephanie R. Barbari

Pancreatic Cancer: Novel Therapy, Research Tools, and Educational Outreach , Ayrianne J. Crawford

Apixaban to Prevent Thrombosis in Adult Patients Treated With Asparaginase , Krishna Gundabolu

Molecular Investigation into the Biologic and Prognostic Elements of Peripheral T-cell Lymphoma with Regulators of Tumor Microenvironment Signaling Explored in Model Systems , Tyler Herek

Utilizing Proteolysis-Targeting Chimeras to Target the Transcriptional Cyclin-Dependent Kinases 9 and 12 , Hannah King

Insights into Cutaneous Squamous Cell Carcinoma Pathogenesis and Metastasis Using a Bedside-to-Bench Approach , Marissa Lobl

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Cooperativity of CCNE1 and FOXM1 in High-Grade Serous Ovarian Cancer , Lucy Elge

Characterizing the critical role of metabolic and redox homeostasis in colorectal cancer , Danielle Frodyma

Genomic and Transcriptomic Alterations in Metabolic Regulators and Implications for Anti-tumoral Immune Response , Ryan J. King

Dimers of Isatin Derived Spirocyclic NF-κB Inhibitor Exhibit Potent Anticancer Activity by Inducing UPR Mediated Apoptosis , Smit Kour

From Development to Therapy: A Panoramic Approach to Further Our Understanding of Cancer , Brittany Poelaert

The Cellular Origin and Molecular Drivers of Claudin-Low Mammary Cancer , Patrick D. Raedler

Mitochondrial Metabolism as a Therapeutic Target for Pancreatic Cancer , Simon Shin

Development of Fluorescent Hyaluronic Acid Nanoparticles for Intraoperative Tumor Detection , Nicholas E. Wojtynek

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The role of E3 ubiquitin ligase FBXO9 in normal and malignant hematopoiesis , R. Willow Hynes-Smith

BRCA1 & CTDP1 BRCT Domainomics in the DNA Damage Response , Kimiko L. Krieger

Targeted Inhibition of Histone Deacetyltransferases for Pancreatic Cancer Therapy , Richard Laschanzky

Human Leukocyte Antigen (HLA) Class I Molecule Components and Amyloid Precursor-Like Protein 2 (APLP2): Roles in Pancreatic Cancer Cell Migration , Bailee Sliker

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FOXM1 Expression and Contribution to Genomic Instability and Chemoresistance in High-Grade Serous Ovarian Cancer , Carter J. Barger

Overcoming TCF4-Driven BCR Signaling in Diffuse Large B-Cell Lymphoma , Keenan Hartert

Functional Role of Protein Kinase C Alpha in Endometrial Carcinogenesis , Alice Hsu

Functional Signature Ontology-Based Identification and Validation of Novel Therapeutic Targets and Natural Products for the Treatment of Cancer , Beth Neilsen

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Understanding the Relationship between TGF-Beta and IGF-1R Signaling in Colorectal Cancer , Katie L. Bailey

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Perturbing anti-apoptotic proteins to develop novel cancer therapies , Jacob Contreras

Role of Ezrin in Colorectal Cancer Cell Survival Regulation , Premila Leiphrakpam

Evaluation of Aminopyrazole Analogs as Cyclin-Dependent Kinase Inhibitors for Colorectal Cancer Therapy , Caroline Robb

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Molecular Mechanisms Regulating MYC and PGC1β Expression in Colon Cancer , Jamie L. McCall

Pancreatic Cancer Invasion of the Lymphatic Vasculature and Contributions of the Tumor Microenvironment: Roles for E-selectin and CXCR4 , Maria M. Steele

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Characterization and target identification of non-toxic IKKβ inhibitors for anticancer therapy , Elizabeth Blowers

Effectors of Ras and KSR1 dependent colon tumorigenesis , Binita Das

Characterization of cancer-associated DNA polymerase delta variants , Tony M. Mertz

A Role for EHD Family Endocytic Regulators in Endothelial Biology , Alexandra E. J. Moffitt

Biochemical pathways regulating mammary epithelial cell homeostasis and differentiation , Chandrani Mukhopadhyay

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LGR5 Activates TGFbeta Signaling and Suppresses Metastasis in Colon Cancer , Xiaolin Zhou

LGR5 Activates TGFβ Signaling and Suppresses Metastasis in Colon Cancer , Xiaolin Zhou

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Genetic dissection of the role of CBL-family ubiquitin ligases and their associated adapters in epidermal growth factor receptor endocytosis , Gulzar Ahmad

Strategies for the identification of chemical probes to study signaling pathways , Jamie Leigh Arnst

Defining the mechanism of signaling through the C-terminus of MUC1 , Roger B. Brown

Targeting telomerase in human pancreatic cancer cells , Katrina Burchett

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Mechanisms of regulation of AID APOBEC deaminases activity and protection of the genome from promiscuous deamination , Artem Georgievich Lada

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Deconvolution of the phosphorylation patterns of replication protein A by the DNA damage response to breaks , Kerry D. Brader

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Analysis of Skin Cancer and Patient Healthcare Using Data Mining Techniques


  • 1 Department of Computational Intelligence, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur 603203, India.
  • 2 School of Computing, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India.
  • 3 Department of Computer Science and Engineering, Veltech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology, Avadi, Chennai, India.
  • 4 Department of ECE, Manakula Vinayagar Institute of Technology, Puducherry, India.
  • 5 Institute of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
  • 6 Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India.
  • 7 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, India.
  • 8 Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa, Ethiopia.
  • PMID: 36199959
  • PMCID: PMC9529455
  • DOI: 10.1155/2022/2250275

Skin cancer is the uncontrolled growth of irregular cancer cells in the human-skin's outer layer. Skin cells commonly grow in an uneven pattern on exposed skin surfaces. The majority of melanomas, aside from this variety, form in areas that are rarely exposed to sunlight. Harmful sunlight, which results in a mutation in the DNA and irreparable DNA damage, is the primary cause of skin cancer. This demonstrates a close connection between skin cancer and molecular biology and genetics. Males and females both experience the same incidence rate. Avoiding revelation to ultraviolet (UV) emissions can lower the risk rate. This needed to be known about in order to be prevented from happening. To identify skin cancer, an improved image analysis technique was put forth in this work. The skin alterations are routinely monitored by this proposed skin cancer categorization approach. Therefore, early detection of suspicious skin changes can aid in the early discovery of skin cancer, increasing the likelihood of a favourable outcome. Due to the blessing of diagnostic technology and recent advancements in cancer treatment, the survival rate of patients with skin cancer has grown. The strategy for detecting skin cancer using image processing technologies is presented in this paper. The system receives the image of the skin lesion as an input and analyses it using cutting-edge image processing methods to determine whether skin cancer is present. The Lesion Image Analysis Tools use texture, size, and shape assessment for image segmentation and feature phases to check for various cancer criteria including asymmetries, borders, pigment, and diameter. The image is classified as Normal skin and a lesion caused by skin cancer using the derived feature parameters.

Copyright © 2022 N. Arivazhagan et al.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Proposed model block diagram.

Algorithm 1

Enhanced image analysis technique (EIAT).

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Sarah Ferguson Gives Health Update, on Why She’s Foregoing Treatment After Skin Cancer Diagnosis

Sarah Ferguson is not undergoing treatment after being diagnosed with skin cancer earlier this year.

"I have to be checked regularly and I have to put cream on my face to get out past sun damage, which means big blisters on my face, chest and hands for three weeks,” Ferguson, 64, told HELLO! in her cover story, published on Sunday, June 2. “But I'm not doing immunotherapy, taking any drugs or doing chemotherapy, for which I'm very grateful."

It's the second cancer battle within a year for the Duchess of York, who is mother to Princess Beatrice and Princess Eugeni e, her daughters with ex-husband Prince Andrew . Us Weekly confirmed in June 2023 that Ferguson was diagnosed with breast cancer . Six months later, a rep for Ferguson revealed that she had been diagnosed with malignant melanoma .

“Her dermatologist asked that several moles were removed and analyzed at the same time as the Duchess was undergoing reconstructive surgery following her mastectomy, and one of these has been identified as cancerous,” a rep for the royal family member said in a statement to Us in January.

Royal Family Members Who Were Diagnosed With Cancer: From the Early Monarchs to King Charles III

Ferguson said in her HELLO! profile that doctors have deterred her from using the phrase "cancer-free,” but she’s keeping a positive outlook regarding her health after beating both breast and skin cancer.

"I have the most exceptional family and I have an extraordinarily great team and I have an enormous ability to turn to joy," she told the publication, noting that she’s always told daughters the truth about her health journey.

“I have always brought up my girls to be so honest and frank that they know I’m going to tell it to them straight, however difficult it is," Ferguson told HELLO! "So, when they said: 'Mummy, tell us the absolute truth — have they got all the cancer out?' and the answer was yes, they knew they were safe."

Beatrice, 35, revealed that her mother was “all in the clear” health-wise during a recent TV appearance.

Sarah Ferguson’s Ups and Downs With the Royal Family: Divorce, Wedding Snubs and More

“She’s such a phenomenal icon. As a mum she’s been amazing, she’s been through so much and her sense of purpose and resilience really keeps me going,” Beatrice said during the May 6 episode of U.K.’s This Morning . “At 64 she’s thriving; she’s been through so much, but I think really now she’s coming into her own.”

It seems Ferguson agrees with her daughter.

"I think it woke me up," she told HELLO! about her cancer diagnoses. "It gave me a swift kick in the butt and told me: 'Right, are you going to start living now, at 64, or are you going to keep on not quite living?' You don't have to be what everyone wants you to be: just be yourself .”

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Cancer diagnoses in the british royal family over the years: ‘the great equalizer’.

Thanks for contacting us. We've received your submission.

King Charles III, 75, has received a cancer diagnosis, Buckingham Palace announced in a statement on Monday.

The cancer was discovered when the king underwent a routine procedure on Jan. 17 to treat an enlarged prostate.

The palace as of now has not confirmed the type or stage of cancer, saying only that it is not prostate cancer. The king began treatments on Monday.

Other British royals have fought their own cancer battles over the years.

Sarah Ferguson, Duchess of York: Breast and skin cancer

Most recently, Sarah Ferguson, Duchess of York, announced on Jan. 21 that she was diagnosed with skin cancer just a month after receiving breast cancer treatments.

“I have been taking some time to myself as I have been diagnosed with malignant melanoma, a form of skin cancer, my second cancer diagnosis within a year after I was diagnosed with breast cancer this summer and underwent a mastectomy and reconstructive surgery,” Ferguson, who is 64, wrote in an Instagram post. 

“It was thanks to the great vigilance of my dermatologist that the melanoma was detected when it was.”

Sarah Ferguson, Duchess of York attending a Christmas morning service, smiling and wearing a green headband. (Photo by Stephen Pond/Getty Images)

King George VI: Lung cancer

King George VI, who took over the throne on Dec. 11, 1936 until his death, was diagnosed with lung cancer in Sept. 1951.

“He was a chain smoker and had been advised by his doctors to smoke to help ‘smooth his lungs’ given his stutter,” said Fordwich.

The longtime smoker underwent surgery to remove his left lung, according to The Independent.

After an initial period of recovery, the king’s health declined and he succumbed to the disease on Feb. 6, 1952, at 56 years old.

King Edward VII: Basal cell carcinoma

King Edward VII, who reigned from Jan. 22, 1901 until his death in 1910, was diagnosed with basal-cell carcinoma, the most common form of skin cancer, in 1907.

His cancer, which was found on the skin next to his nose, was reportedly cured with radium.

After suffering additional health issues later in life, Edward died at 68 years of age on May 6, 1910, after a series of heart attacks.

Princess Victoria: Breast cancer

The daughter of Queen Victoria and Prince Albert, Princess Victoria, who was born on Nov. 21, 1840, was diagnosed with breast cancer in 1898.

The mother of eight died of the disease on Aug. 5, 1901, at the age of 60.

“There is no family left untouched by cancer,” said Dr. Nathan Goodyear, the medical director at Brio Medical, a holistic, integrative cancer healing center in Scottsdale, Arizona.

“Cancer knows no preferences,” he told Fox News Digital. “Whether left or right, conservative or liberal, upper class or lower class, cancer shows no leaning.”

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Likewise, cancer knows no difference between those of royal descent and those of non-royal descent, he added.

“Despite access to the most innovative medical care and brightest minds in the world, royal families still encounter cancer,” Goodyear said. 

“When it comes to demographics, cancer is the great equalizer.”

“Yet, whether royal or non-royal, look up, pray and take heart — because hope is present, and when hope is present, healing is possible.”

Earlier this week, Her Majesty the Queen opened Maggie’s Royal Free, a new cancer support center at Royal Free Hospital in London, as announced on the royal family’s website. 

Maggie’s provides free care and support for cancer patients, their friends and families in the U.K. and online.

Share this article:

Sarah Ferguson, Duchess of York attending a Christmas morning service, smiling and wearing a green headband. (Photo by Stephen Pond/Getty Images)



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  17. Skin Cancer and Transition Statement Essay

    Thesis Statement: Skin cancer is increasing rapidly among teens and adults', knowing what skin cancer is, the symptoms and how to reduce your risks of getting skin cancer at an early age. ... Transition statement: once we know what skin cancer is and the symptoms are we can learn what causes these skin cancers. B. There are many causes of ...

  18. Theses & Dissertations: Cancer Research

    Functions and regulation of Ron receptor tyrosine kinase in human pancreatic cancer and its therapeutic applications, Yi Zou. Theses/Dissertations from 2011 PDF. Coordinate detection of new targets and small molecules for cancer therapy, Kurt Fisher. PDF. The role of c-Myc in pancreatic cancer initiation and progression, Wan-Chi Lin

  19. Analysis of Skin Cancer and Patient Healthcare Using Data Mining

    The strategy for detecting skin cancer using image processing technologies is presented in this paper. The system receives the image of the skin lesion as an input and analyses it using cutting-edge image processing methods to determine whether skin cancer is present. The Lesion Image Analysis Tools use texture, size, and shape assessment for ...

  20. PDF Best Nursing Practices in Caring for Patients With Breast Cancer Genes

    thesis seeks to develop a protocol for the best-practices of caring for individuals who are carriers of the BRCA1 or BRCA2 mutations. Significance of the Problem Breast cancer is the second most common cancer in women, surpassed only by skin cancer (National Institute of Health, 2019). In 2019, approximately 268,000 women were

  21. Skin cancer thesis statement º NCC Dept. of MAT/CSC/ITE

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  22. Sarah Ferguson Gives Health Update, on Why She's Foregoing ...

    Sarah Ferguson is not undergoing treatment after being diagnosed with skin cancer earlier this year. "I have to be checked regularly and I have to put cream on my face to get out past sun damage ...

  23. Cancer diagnoses in the British royal family over the years: 'The great

    Published Feb. 6, 2024, 5:57 p.m. ET. King Charles III, 75, has received a cancer diagnosis, Buckingham Palace announced in a statement on Monday. The cancer was discovered when the king underwent ...