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Artificial neural networks allow response prediction in squamous cell carcinoma of the scalp treated with radiotherapy
Author(s) -
Damiani G.,
Grossi E.,
Berti E.,
Conic R.R.Z.,
Radhakrishna U.,
Pacifico A.,
Bragazzi N.L.,
Piccinno R.,
Linder D.
Publication year - 2020
Publication title -
journal of the european academy of dermatology and venereology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 107
eISSN - 1468-3083
pISSN - 0926-9959
DOI - 10.1111/jdv.16210
Subject(s) - medicine , radiation therapy , scalp , lymph node , stage (stratigraphy) , retrospective cohort study , recursive partitioning , observational study , surgery , oncology , paleontology , biology
Background Epithelial neoplasms of the scalp account for approximately 2% of all skin cancers and for about 10–20% of the tumours affecting the head and neck area. Radiotherapy is suggested for localized cutaneous squamous cell carcinomas ( cSCC ) without lymph node involvement, multiple or extensive lesions, for patients refusing surgery, for patients with a poor general medical status, as adjuvant for incompletely excised lesions and/or as a palliative treatment. To date, prognostic risk factors in scalp cSCC patients are poorly characterized. Objective To identify patterns of patients with higher risk of postradiotherapy recurrence. Methods A retrospective observational study was performed on scalp cSCC patients with histological diagnosis who underwent conventional radiotherapy (50–120 kV) (between 1996 and 2008, follow‐up from 1 to 140 months, median 14 months). Out of the 79 enrolled patients, 22 (27.8%) had previously undergone a surgery. Two months after radiotherapy, 66 (83.5%) patients achieved a complete remission, 6 (7.6%) a partial remission, whereas 2 (2.5%) proved non‐responsive to the treatment and 5 cases were lost to follow‐up. Demographical and clinical data were preliminarily analysed with classical descriptive statistics and with principal component analysis. All data were then re‐evaluated with a machine learning‐based approach using a 4th generation artificial neural networks ( ANN s)‐based algorithm. Results Artificial neural networks analysis revealed four scalp cSCC profiles among radiotherapy responsive patients, not previously described: namely, (i) stage T2 cSCC type, aged 70–80 years; (ii) frontal cSCC type, aged <70 years; (iii) non‐recurrent nodular or nodulo‐ulcerated, stage T3 cSCC type, of the vertex and treated with >60 Grays (Gy); and (iv) flat, occipital, stage T1 cSCC type, treated with 50–59 Gy. The model uncovering these four predictive profiles displayed 85.7% sensitivity, 97.6% specificity and 91.7% overall accuracy. Conclusions Patient profiling/phenotyping with machine learning may be a new, helpful method to stratify patients with scalp cSCC s who may benefit from a RT ‐treatment.