Faculty Bibliography

Median canaliform dystrophy (MCD) of Heller is a rare nail disorder characterized by a median split and obliquely running furrows of the nail plate, often presenting in a "fir-tree" pattern. While its pathogenesis remains poorly understood, proposed etiologies include habitual trauma, iatrogenic causes, and familial predisposition. Diagnosis is generally made by clinical evaluation. There is a paucity of data regarding the histopathology of MCD. Herein, we present two cases of MCD with novel histopathologic features, broadening the understanding of the pathophysiology and potential therapeutic options for this condition. In Case 1, histopathologic evaluation revealed mild spongiosis, suggesting an inflammatory etiology. Case 2 demonstrated distinctive acantholysis. These findings suggest that MCD encompasses a spectrum of histopathologic changes. Our report contributes to the limited histopathologic characterization of MCD.

BACKGROUND:Satellitosis or in-transit metastasis (S-ITM) from cutaneous squamous cell carcinoma (cSCC) is associated with poor outcomes but is not included in current staging guidelines. OBJECTIVE:To determine risk factors and prognostic significance of S-ITM. METHODS:This cohort study included 8,901 patients with cSCC from 12 institutions (1998-2023). Risk factors for S-ITM were calculated using logistic regression. Outcomes were compared with 1:2 propensity score matched controls using a Fine-Gray subdistribution hazard model. RESULTS:Seventy-seven patients developed S-ITM. Increased patient age (OR 1.03, 95% CI 1.01-1.05, p<0.01), history of immunosuppression (OR 4.31, 95% CI 2.59-7.10, p<0.001), higher BWH stage (T2a OR 4.14, 95% CI 2.05-8.41; T2b OR 15.96, 95% CI 8.58-31.19; T3 OR 30.27, 95% CI 10.70-79.04, all p<0.001) and LVI (OR 4.57, 95% CI 1.80-10.38, p=0.001) were independent risk factors for S-ITM. S-ITM was associated with LR (SHR 2.40, 95% CI 1.43-4.04, p<0.001), NM (SHR 1.89 (95% CI .02-3.49, p=0.04), DM (SHR 4.41, 95% CI 1.45-13.27, p=0.01), and DSD (SHR 4.48, 95% CI 2.34-8.58, p<0.001). LIMITATIONS/CONCLUSIONS:Retrospective cohort study. The rarity of S-ITM may limit statistical power. CONCLUSION/CONCLUSIONS:Patients with cSCC and S-ITM are at higher risk for poor outcomes independent of patient, tumor, and treatment characteristics.

IMPORTANCE/UNASSIGNED:There exists substantial heterogeneity in outcomes within T stages for patients with cutaneous squamous cell carcinoma (cSCC). OBJECTIVE/UNASSIGNED:To determine whether a customized generative pretrained transformer model, trained on a comprehensive dataset with more than 1 trillion parameters and equipped with relevant focused context and retrieval augmented generation (RAG), could excel in aggregating and interpreting vast quantities of data to develop a novel class-based risk stratification system that outperforms the current standards. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:To build the RAG knowledge base, a systematic review of the literature was conducted that addressed risk factors for poor outcomes in cSCC. Using the RAG-enabled generative pretrained transformer (GPT) model, we developed a novel class-based risk stratification system that assigned point values for risk factors, culminating in a GPT-based prognostication system called the artificial intelligence-derived risk score (AIRIS). The system's performance was validated on a combined prospective and retrospective cohort of 2379 primary cSCC tumors (1996-2023) with at least 36 months of follow-up, against Brigham and Women's Hospital (BWH) and American Joint Committee on Cancer Staging Manual, eighth edition (AJCC8) systems in stratifying risk for locoregional recurrence (LR), nodal metastasis (NM), distant metastasis (DM), and disease-specific death (DSD). MAIN OUTCOMES AND MEASURES/UNASSIGNED:Performance metrics evaluated included distinctiveness, homogeneity, and monotonicity, as defined by the AJCC8, as well as sensitivity, specificity, positive predictive value, negative predictive value, accuracy, the area under the receiver operating characteristic curve, and concordance. RESULTS/UNASSIGNED:The median age at diagnosis was 73 (IQR, 64-81) years, with 38.5% female patients and 61.5% male patients. The AIRIS prognostication system demonstrated superior sensitivity across all outcomes (LR, 49.1%; NM, 73.7%; DM, 82.5%; and DSD, 72.2%) and the highest area under the receiver operating characteristic curve values (LR, 0.69; NM, 0.81; DM, 0.85; and DSD, 0.80), indicating significantly enhanced discriminative capability compared with the BWH and AJCC8 systems. While all systems were comparably distinctive, the AIRIS prognostication system consistently demonstrated the lowest proportion of tumors exhibiting poor outcomes in low-risk categories, suggesting its improved homogeneity and monotonicity. CONCLUSIONS AND RELEVANCE/UNASSIGNED:The results of this diagnostic study suggest that the AIRIS system outperforms the existing BWH and AJCC8 prognostication systems, potentially providing a more effective tool for predicting poor outcomes in cSCC. This study illustrates the potential of large language models in refining prognostic tools, offering implications for treating patients with cancer.

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of 0.73 in the development cohort and 0.84 in the Mayo cohort. The model's interpretability revealed that features like poor differentiation and deep invasion were strongly associated with poor prognosis. Furthermore, the model is effective in stratifying risk among BWH T2a and AJCC T2, known for outcome heterogeneity.