Artificial intelligence-empowered cellular morphometric risk score improves prognostic stratification of cutaneous squamous cell carcinoma

Author:

Pérez-Baena Manuel J12,Mao Jian-Hua34,Pérez-Losada Jesús12,Santos-Briz Ángel25,Chang Hang34,Cañueto Javier126ORCID

Affiliation:

1. Instituto de Biología Molecular y Celular del Cáncer, Universidad de Salamanca/CSIC , Salamanca , Spain

2. Instituto de Investigación Biomédica de Salamanca

3. Berkeley Biomedical Data Science Center

4. Biological Systems and Engineering Division; Lawrence Berkeley National Laboratory , Berkeley, CA , USA

5. Servicio de Anatomía Patológica

6. Servicio de Dermatología; Complejo Asistencial Universitario de Salamanca , Salamanca , Spain

Abstract

Abstract Background Risk stratification of cutaneous squamous cell carcinoma (cSCC) is essential for managing patients. Objectives To determine if artificial intelligence and machine learning might help to stratify patients with cSCC by risk using more than solely clinical and histopathological factors. Methods We retrieved a retrospective cohort of 104 patients whose cSCCs had been excised with clear margins. Clinical and histopathological risk factors were evaluated. Haematoxylin and eosin-stained slides were scanned and analysed by an algorithm based on the stacked predictive sparse decomposition technique. Cellular morphometric biomarkers (CMBs) were identified via machine learning and used to derive a cellular morphometric risk score (CMRS) that classified cSCCs into clusters of differential prognoses. Concordance analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated and compared with results obtained with the Brigham and Women’s Hospital (BWH) staging system. The performance of the combination of the BWH staging system and the CMBs was also analysed. Results There were no differences among the CMRS groups in terms of clinical and histopathological risk factors and T-stage assignment, but there were significant differences in prognosis. Combining the CMRS with BWH staging systems increased distinctiveness and improved prognostic performance. C-indices were 0.91 local recurrence and 0.91 for nodal metastasis when combining the two approaches. The NPV was 94.41% and 96.00%, the PPV was 36.36% and 41.67%, and accuracy reached 86.75% and 89.16%, respectively, with the combined approach. Conclusions CMRS is helpful for cSCC risk stratification beyond classic clinical and histopathological risk features. Combining the information from the CMRS and the BWH staging system offers outstanding prognostic performance for patients with high-risk cSCC.

Funder

National Cancer Institute at the National Institutes of Health

Lawrence Berkeley National Laboratory

University of California

Department of Energy

European Union Next Generation EU/PRTR

Regional Government of Castile and León

Instituto de Salud Carlos III

Programa de Intensificación

Publisher

Oxford University Press (OUP)

Subject

Dermatology

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Automated cutaneous squamous cell carcinoma grading using deep learning with transfer learning;Romanian Journal of Morphology and Embryology;2024-07-15

2. Widening the scope of artificial intelligence applications in dermatology;Clinical and Experimental Dermatology;2024-05-10

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