Development of a scoring system via an interpretable end-to-end neural network for prognostic stratification of patients with advanced melanoma

IF 18 1区医学 Q1 HEALTH CARE SCIENCES & SERVICES

NPJ Digital Medicine Pub Date : 2026-10-13 DOI:10.1038/s41746-026-03379-7

Ruihao Huang,Hao Zhu,Yue Huang,Hanrui Zhang,Yuanfang Guan,Menglun Wang,Lixia Zhang,Yutao Gong,Hong Zhao,Shiew-Mei Huang,Issam Zineh,Yuan Li Shen,Ge Feng,Steven Lemery,Marc R. Theoret,Lola Fashoyin-Aje,Shenggang Wang,Mo Tiwari,James Zou,Sebastian Thrun,Qi Liu

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引用次数: 0

Abstract

Abstract We created an interpretable end-to-end neural network (ScoreNet) which can be used to develop an easy-to-implement scoring system for risk stratification. We applied it to data from 2711 patients with advanced melanoma across 5 trials that supported new drug approvals and developed a scoring system for overall survival (OS) and progression-free survival (PFS). The scoring system was then validated in two separate trials. This score is a weighted summation based on eight baseline features (sum of longest diameter for all target lesions, lactate dehydrogenase, systemic immune inflammation index, hemoglobin, ECOG performance status, liver metastasis, pulse rate, and PD-L1 immunohistochemistry status). Using the score, patients can be classified into low, moderate, or high-risk categories of reduced OS and PFS. Risk stratification was demonstrated for patients treated with PD-1/PD-L1 inhibitors, chemotherapy, CTLA-4 inhibitors, BRAF or MEK inhibitors, and their combinations. ScoreNet-based risk stratification can be a new tool for clinical research/practice.

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通过可解释的端到端神经网络对晚期黑色素瘤患者进行预后分层的评分系统的开发

摘要建立了一个可解释的端到端神经网络（ScoreNet），该网络可用于开发易于实现的风险分层评分系统。我们将其应用于支持新药批准的5项试验中2711例晚期黑色素瘤患者的数据，并开发了总生存期（OS）和无进展生存期（PFS）评分系统。评分系统随后在两个独立的试验中得到验证。该评分是基于八个基线特征（所有靶病变最长直径之和、乳酸脱氢酶、全身免疫炎症指数、血红蛋白、ECOG表现状态、肝转移、脉搏率和PD-L1免疫组织化学状态）的加权总和。根据评分，可以将患者分为低、中、高风险OS和PFS降低的类别。在接受PD-1/PD-L1抑制剂、化疗、CTLA-4抑制剂、BRAF或MEK抑制剂及其联合治疗的患者中证实了风险分层。基于评分网的风险分层可以成为临床研究/实践的新工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

NPJ Digital Medicine Multiple-

CiteScore

25.10

自引率

3.30%

发文量

170

审稿时长

15 weeks

期刊介绍： npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.