Pham, Nancy

Importance There is no consensus on staging and surveillance imaging for cutaneous squamous cell carcinoma (CSCC). Although imaging has been shown to affect management, broad recommendations from the National Comprehensive Cancer Network have led to variability in clinical practice. Objective To develop multidisciplinary consensus recommendations on the use of staging and surveillance imaging of localized CSCC. Evidence Review A multidisciplinary expert panel across academic and clinical practice settings convened a Delphi consensus, with 3 iterative survey rounds from January to June 2025. Data were analyzed from February 2025 to January 2026. Eligibility defined by at least 1 to 2 CSCC publications during the previous 5 years, involvement in a relevant clinical trial, experience treating at least 3 patients monthly with CSCC, and/or 5 years or longer of clinical practice. Fifty-four experts were invited and 45 (83%) completed all 3 rounds and were included in the final analysis. Structured Delphi surveys evaluated clinical scenarios, imaging modalities, and surveillance strategies for CSCC. Consensus and near-consensus recommendations were generated, defined as 80% or greater and 70% to 79% agreement, respectively. Findings The 45 participants (21 female individuals [47%] and 24 male individuals [53%]; 14 Asian individuals [31%], 1 multiracial individual [2%], and 30 White individuals [67%]) consisted of dermatology (15 [33%]), medical oncology (7 [16%]), radiation oncology (9 [20%]), radiology (9 [20%]), surgery (3 [7%]), and otolaryngology specialists (2 [4%]). The Delphi panel recommended staging and surveillance for CSCCs with at least a 15% risk of metastasis. Consensus or near consensus was reached to recommend staging and surveillance imaging for tumors with a concern for metastasis, bone invasion, invasion beyond subcutaneous fat, large-caliber nerve invasion, or a diameter of 4 cm or larger or the combination of tumors with poorly differentiated histology and any of the following: diameter of 2 cm or larger, lymphovascular invasion and subcutaneous fat invasion, or lymphovascular invasion and small-caliber perineural invasion. Computed tomography imaging was the preferred modality for nodal staging (38 [84%]) and surveillance (35 [78%]). For surveillance duration, consensus was reached to provide imaging for at least 2 years, and near consensus was reached for at least 3 years. Conclusions and Relevance This study provides an expert consensus–based framework to standardize imaging in localized CSCC that may inform future guidelines.

Artificial Intelligence (AI) has emerged as a transformative force in orthopedic oncology, offering significant advances in the diagnosis, classification, and prediction of treatment response for primary malignant bone tumors (PBT). Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. The integration of radiomics with AI enables the extraction of quantitative features from medical images, allowing for precise tumor characterization and the development of personalized therapeutic strategies. Notably, convolutional neural networks have demonstrated exceptional capabilities in pattern recognition, significantly improving tumor detection, segmentation, and differentiation. This narrative review synthesizes the evolving applications of AI in PBTs, focusing on early tumor detection, imaging analysis, therapy response prediction, and histological classification. AI-driven radiomics and predictive models have yielded promising results in assessing chemotherapy efficacy, optimizing preoperative imaging, and predicting treatment outcomes, thereby advancing the field of precision medicine. Innovative segmentation techniques and multimodal imaging models have further enhanced healthcare efficiency by reducing physician workload and improving diagnostic accuracy. Despite these advancements, challenges remain. The rarity of PBTs limits the availability of robust, high-quality datasets for model development and validation, while the lack of standardized imaging protocols complicates reproducibility. Ethical considerations, including data privacy and the interpretability of complex AI algorithms, also warrant careful attention. Future research should prioritize multicenter collaborations, external validation of AI models, and the integration of explainable AI systems into clinical practice. Addressing these challenges will unlock AI’s full potential to revolutionize PBT management, ultimately improving patient outcomes and advancing personalized care.