Analysis of national skin cancer expenditures in the United States Medicare population, 2013

Ruiz, Emily S.; Morgan, Frederick; Zigler, Corwin; Besaw, Robert; Schmults, Chrysalyne D.

doi:10.1016/j.jaad.2018.04.035

Cited by 20 publications

(19 citation statements)

References 3 publications

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“…To the Editor: Studies have suggested that patients with psoriasis have a higher risk of hospitalization for acute infections. [1][2][3][4] However, the impact of psoriasis on milder cases of infection treated in outpatient settings is unknown. We aimed to examine the association of psoriasis with the risk of acute infection including inpatient, outpatient, and emergency department visits and whether the risk of acute infection was associated with systemic antipsoriatic treatment using a large national cohort.…”

Section: Risk Of Acute Infections In Patients With Psoriasis: a Natiomentioning

confidence: 99%

“…1 In 2013, the annualized per-patient Medicare spending for ipilimumab was more than $80,000, whereas lifetime treatment with alternative therapies was less than $50,000. 2,3 After ipilimumab was approved and through 2015, other costly immunotherapies (programmed cell death protein 1 inhibitors) and targeted therapies (BRAF and MEK inhibitors) were approved. This cross-sectional analysis evaluates Medicare spending, out-of-pocket costs (OOPC), and utilization trends for immunotherapy, targeted therapy, and other melanoma treatments in 2013 and 2015, before the standard use of dual biologic regimens.…”

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confidence: 99%

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Cost and utilization of immunotherapy and targeted therapy for melanoma: Cross-sectional analysis in the Medicare population, 2013 and 2015

Seiger

Schmults

Silk

et al. 2020

Journal of the American Academy of Dermatology

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Section: Risk Of Acute Infections In Patients With Psoriasis: a Natiomentioning

confidence: 99%

mentioning

confidence: 99%

Cost and utilization of immunotherapy and targeted therapy for melanoma: Cross-sectional analysis in the Medicare population, 2013 and 2015

Seiger

Schmults

Silk

et al. 2020

Journal of the American Academy of Dermatology

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“…UV-induced skin cancer is another outcome of chronic UV exposure associated with 90% of cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) cases and 65% of cutaneous melanoma cases (Kim and He, 2014;Linos et al, 2009). In total, they account for 3.5 million new cancer cases, nearly 15,000 deaths, and over $3 billion of medical costs annually in the United States, posing a major public health issue and economic burden (Rogers et al, 2015;Ruiz et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

TGFβ Signaling in Photoaging and UV-Induced Skin Cancer

Wang

2021

Journal of Investigative Dermatology

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“…According to the statistics reported by the American skin cancer society [4], melanoma is the 19 th most commonly found problem worldwide, and 100,350 new cases of melanoma were anticipated in the USA, 16221 in Australia, and 2500 in New Zealand in 2020 [5]. In terms of pricing, it has been estimated that the cost of treating skin cancer is 3.3 billion per year [6,7]; thus, it is the most expensive procedure for the health systems.…”

Section: Introductionmentioning

confidence: 99%

Automatic Lesion Segmentation Using Atrous Convolutional Deep Neural Networks in Dermoscopic Skin Cancer Images

Kaur

GholamHosseini

Sinha

2021

Preprint

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Background: Among skin cancers, melanoma is the most dangerous and aggressive form, exhibiting a high mortality rate worldwide. Biopsy and histopatholog-ical analysis are common procedures for skin cancer detection and prevention in clinical settings. A significant step involved in the diagnosis process is the deep understanding of patterns, size, color, and structure of lesions based on images obtained through dermatoscopes for the infected area. However, the manual seg-mentation of the lesion region is time-consuming because the lesion evolves and changes its shape over time which makes its prediction challenging. Moreover, at the initial stage, it is difficult to predict melanoma as it closely resembles other skin cancer types that are not malignant as melanoma, thus automatic segmentation techniques are required to design a computer-aided system for accurate and timely detection. Methods: As deep learning approaches have gained high attention in recent years due to their remarkable performance, therefore, in this work, we proposed a novel, end-to-end atrous spatial pyramid pooling based convolutional neural network (CNN) framework for automatic lesion segmentation. This architecture is built based on the concept of atrous dilated convolutions which are effective for semantic segmentation. A dense deep neural network is designed using several building blocks consisting of convolutional, batch normalization, leaky ReLU layer with fine-tuning of hyperparameters contributing towards higher performance. Conclusion: The network was tested using three benchmark datasets by International Skin Imaging Collaboration, i.e. ISIC 2016, ISIC 2017, and ISIC 2018. The experimental results showed that the proposed network achieved an average Jac-card index of 86.5% on ISIC 2016, 81.2% on ISIC 2017, and 81.2% on ISIC 2018 datasets, respectively which is recorded as higher than the top three winners of the ISIC challenge. Also, the model successfully extracts lesions from the whole image in one pass, requiring no pre-processing process. The conclusions yielded that network is accurate in performing lesion segmentation on skin cancer images.

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Analysis of national skin cancer expenditures in the United States Medicare population, 2013

Cited by 20 publications

References 3 publications

Cost and utilization of immunotherapy and targeted therapy for melanoma: Cross-sectional analysis in the Medicare population, 2013 and 2015

Cost and utilization of immunotherapy and targeted therapy for melanoma: Cross-sectional analysis in the Medicare population, 2013 and 2015

TGFβ Signaling in Photoaging and UV-Induced Skin Cancer

Automatic Lesion Segmentation Using Atrous Convolutional Deep Neural Networks in Dermoscopic Skin Cancer Images

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