Artificial Neural Networks allow Response Prediction in Squamous Cell Carcinoma of the Scalp Treated with Radiotherapy

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Giovanni Damiani 1,2,3,4,^,*, Enzo Grossi ^,5, Emilio Berti 1,2, Rosalynn RZ Conic 4, Uppala Radhakrishna 6, Alessia Pacifico 7, Nicola L Bragazzi 8, Roberta Piccinno 1,2, † and Michael Dennis Linder 9 †,*

Artificial Neural Networks allow Response Prediction in Squamous Cell Carcinoma of the Scalp Treated with Radiotherapy (2020)

In press, Journal of the European Academy of Dermatology and Venereology

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Background
Epithelial neoplasms of the scalp account for approximately 2% of all skin cancers and for about 10-20% of the tumors 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 post-radiotherapy 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 analyzed 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(ANNs)-based algorithm.

Results
ANNs analysis revealed four scalp cSCC profiles among radiotherapy responsive patients, not previously described: namely, 1) stage T2 cSCC type, aged 70-80 years; 2) frontal cSCC type, aged <70 years; 3) non-recurrent nodular or nodulo-ulcerated, stage T3 cSCC type, of the vertex and treated with >60 Grays (Gy); and 4) 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 cSCCs who may benefit from a RT-treatment.

Notes:

1 Phototherapy Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
2 Dermatology Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico di Milano, Dipartimento di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università degli Studi di Milano, Milan, Italy
3 Young Dermatologists Italian Network, Centro Studi GISED, Bergamo, Italy
4 Department of Dermatology, Case Western Reserve University, Cleveland, Ohio, USA
5 Fondazione Villa Santa Maria, Tavernerio, Como, Italy
6 Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, Michigan, USA
7 Clinical Dermatology Department, IRCCS S. Gallicano Dermatological Institute, Rome, Italy
8 School of Public Health, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
9 Ben Gurion University of the Negev, Beer Sheva Israel
^ Equally contributed as first authors.
† Equally contributed as last authors.