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Background. This study presents a comparative analysis of recently published guidelines to manage cutaneous squamous cell carcinoma (cSCC) and cutaneous basal cell carcinoma (cBCC) within the United States (US). Methods. A PubMed database search was performed for the time period between June 1, 2016, and December 1, 2022. A comprehensive comparison was performed in the following clinical interest areas: staging and risk stratification, management of primary tumor and regional nodes with curative intent, and palliative treatment. Results. Guidelines from 3 organizations were analyzed: the American Academy of Dermatology (AAD), the National Comprehensive Cancer Network (NCCN), and the American Society for Radiation Oncology (ASTRO). The guidelines used different methodologies to grade evidence, making comparison difficult. There was agreement that surgery is the preferred treatment for curative cBCC and cSCC. For patients ineligible for surgery, there was a consensus to recommend definitive radiation. AAD and NCCN recommended consideration of other topical modalities in selected low-risk cBCC. Postoperative radiation therapy (PORT) was uniformly recommended in patients with positive margins that could not be cleared with surgery and in patients with nerve invasion. The definition and extent of nerve invasion varied. All guidelines recommended surgery as the primary treatment in patients with lymph node metastases in a curative setting. The criteria used for PORT varied; NCCN and ASTRO used lymph node size, number of nodes, and extracapsular extension for recommending PORT. Both NCCN and ASTRO recommend consideration of systemic treatment along with PORT in patients with extracapsular extension. Conclusion: US guidelines provide contemporary and complementary information on the management of cBCC and cSCC. There are opportunities for research, particularly in the areas of staging, indications for adjuvant treatment in curative settings, extent of nerve invasion and prognosis, and the role of systemic treatments in curative and palliative settings.

Background and purpose: The ability to determine the risk and predictors of lymphedema is vital in improving the quality of life for head and neck (HN) cancer patients. However, selecting robust features is challenging due to the multicollinearity and high dimensionality of radiotherapy (RT) data. This study aims to overcome these challenges using an ensemble feature selection technique with machine learning (ML). Materials and methods: Thirty organs-at-risk, including bilateral cervical lymph node levels, were contoured, and dose-volume data were extracted from 76 HN treatment plans. Clinicopathologic data was collected. Ensemble feature selection was used to reduce the number of features. Using the reduced features as input to ML and competing risk models, internal and external lymphedema prediction capability was evaluated with the ML models, and time to lymphedema event and risk stratification were estimated using the risk models. Results: Two ML models, XGBoost and random forest, exhibited robust prediction performance. They achieved average F1-scores and AUCs of 84 ± 3.3 % and 79 ± 11.9 % (external lymphedema), and 64 ± 12 % and 78 ± 7.9 % (internal lymphedema). Predictive ML and risk models identified common predictors, including bulky node involvement, high dose to various lymph node levels, and lymph nodes removed during surgery. At 180 days, removing 0–25, 26–50, and > 50 lymph nodes increased external lymphedema risk to 72.1 %, 95.6 %, and 57.7 % respectively (p = 0.01). Conclusion: Our approach, involving the reduction of HN RT data dimensionality, resulted in effective ML models for HN lymphedema prediction. Predictive dosimetric features emerged from both predictive and competing risk models. Consistency with clinicopathologic features from other studies supports our methodology.