Use of artificial intelligence to identify high risk profiles in early stage melanoma patients from pathology slides.

C Christina Kanaan (Department of Pathology, Gustave Roussy, Villejuif, France) C Céline Bossard (Pathology Department, IHP Group, Nantes, France) S Séverine Roy N Naima Benannoune (Department of Cancer Medicine, Gustave Roussy Cancer Campus, Villejuif, France) N Naima Hamoudi (Gustave Roussy, Villejuif, France) I Ines Besraoui (Department of Cancer Medicine, Gustave Roussy Cancer Campus, Villejuif, France) M Magali Lacroix-Triki J Jérôme Chetritt (Pathology Department, IHP Group, Nantes, France) C Caroline Robert

Abstract

9579 Background: To refine prognostication in primary cutaneous melanoma (CM) and optimize adjuvant treatment decisions, we evaluate SmartProg-MEL (SPM), an AI-based histology-driven algorithm developed by DiaDeep and applied to H&E-stained whole-slide images (WSI) at the time of diagnosis. SPM provides a risk stratification score, under 15 minutes, for overall survival (OS) and relapse-free (RFS) outcomes to support clinical decision-making. Methods: SPM was evaluated on a retrospective cohort of 383 primary CM with 5-year follow-up (46% IA, 15% IB, 9% IIA, 7% IIB, 6% IIC, 13% III, 4% IV). The model stratifies patients into high- or low-risk groups based solely on the WSI of the primary tumor. Kaplan-Meier curves are used to compare RFS and OS between risk groups, with statistical significance assessed using the log-rank test. A multivariable Cox regression analysis was performed to evaluate the independent prognostic value of SPM after adjusting for pathological factors. The negative and positive predictive values (NPV and PPV) of the SMP are explored. Results: Patients with a low risk score had a significantly higher 5-y OS and RFS than patients of the high risk group (93.1% vs 62.5%, p<0.001 and 92.8% vs 47.1%, p<0.001). In multivariable analysis, SPM risk score was the strongest predictor (OS: HR=3.95, p<0.005, RFS: HR=5.03, p<0.005). In the I-IIA group, 29% (n=78) were assigned to the high risk profile with a decrease of the OS and RFS compared with the low risk group (OS: 95.4% vs 86%, p<0.05; RFS: 94.3% vs 74%, p<0.01). SPM has a NPV of 96%, 100% and a PPV of 17% and 69% in stages I/IIA and IIB/C respectively. Conclusions: The AI-based risk stratification algorithm, SPM, demonstrates greater performance than stage in identifying high and low risk profiles, especially in early-stage CM patients. This new prognostic tool opens avenues for a routine clinical application to precise therapeutic decisions in an adjuvant setting. Cohort statistics: Event occurrences and five-year OS and RFS endpoints survival rates, segmented by AJCC stage and SPM risk stratification. # patients RFS OS # events (%) 5-y RFS [CI] # events 5-y OS [CI] I/IIA 266 21 88.1 [82.1 to 92.2] 14 92.5 [87.6 to 95.6] SPM Low Risk 188 (70.7%) 8(38.1%) 94.3 [88.9 to 97.2] 5(35.7%) 95.4 [89.8 to 98.1] SPM High Risk 78(29.3%) 13(61.9%) 74.1 [58.4 to 84.4] 9 (64.3%) 86.1[73.8 to 92.8] IIB/IIC 51 34 24.3 [11.7 to 39.3] 22 47.8 [31.2 to 62.7] SPM Low Risk 1 (2.0%) 0(0.0%) 100 N/D 0 (0.0%) 100 N/D SPM High Risk 50(98.0%) 34(100.0%) 22 [9.7 to 37.4] 22 (100.0%) 46.6 [29.9 to 61.7] III 49 39 32.8 [19.3 to 47.2] 25 100 N/D SPM Low Risk 5(10.2%) 2(5.1%) 60.1 [12.6 to 88.2] 2(8.0%) 60.1 [12.6 to 88.2] SPM High Risk 44(89.8%) 34(87.2%) 29.5 [14.4 to 42.3] 20 (80.0%) 51.4 [35.2 to 65.4] SPM: DiaDeep SmartProg-MEL; CI: confidence intervals.

Article Details

Volume / Issue Vol. 43, Issue 16_suppl
Published June 01, 2025
Pages 9579-9579
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (9)

C

Christina Kanaan

Department of Pathology, Gustave Roussy, Villejuif, France

C

Céline Bossard

Pathology Department, IHP Group, Nantes, France

S

Séverine Roy

N

Naima Benannoune

Department of Cancer Medicine, Gustave Roussy Cancer Campus, Villejuif, France

N

Naima Hamoudi

Gustave Roussy, Villejuif, France

I

Ines Besraoui

Department of Cancer Medicine, Gustave Roussy Cancer Campus, Villejuif, France

M

Magali Lacroix-Triki

J

Jérôme Chetritt

Pathology Department, IHP Group, Nantes, France

C

Caroline Robert