Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC - Scorecard - MDSpire
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Evaluation of an Explainable AI Decision Support Tool and the Effectiveness of Multimodal Models in Non-Small Cell Lung Cancer Management

  • By

  • Arsela Prelaj

  • Vanja Miskovic

  • Matteo Sacco

  • Alberto Ferrarin

  • Cristina Maria Licciardello

  • Leonardo Provenzano

  • Margherita Favali

  • Ludovica Lerma

  • Aleksandra Zec

  • Andrea Spagnoletti

  • Monica Ganzinelli

  • Daniele Lorenzini

  • Beshoy Guirges

  • Luca Invernizzi

  • Cecilia Silvestri

  • Laura Mazzeo

  • Marco Meazza Prina

  • Giulia Corrao

  • Margherita Ruggirello

  • Andra Diana Dumitrascu

  • Rosa Maria Di Mauro

  • Dario Monzani

  • Gabriella Pravettoni

  • Michele Zanitti

  • Davide Macocchi

  • Moreno Bruno Marino

  • Chiara Cavalli

  • Rebecca Romanò

  • Claudia Giani

  • Samuel G. Armato

  • Alessandra Esposito

  • Christine M. Bestvina

  • Maria Spector

  • Bogot R. Naama

  • Reham Basheer

  • Adi Lahiani Hafzadi

  • Laila Roisman

  • Iris Watermann

  • Marlen Szewczyk

  • Till Olchers

  • Heinz Richter

  • Constantin Blanke-Roeser

  • Costanza Siniscalchi

  • Anna Di Lello

  • Teresa Arangoa

  • Valentina Bartolomeo

  • Nikolaos Spathas

  • Evangelos Sarris

  • Elena Fountzilas

  • Aina Arbusà Roca

  • Rocio Caro-Consuegra

  • Patricia Iranzo

  • Melissa Fernández-Pinto

  • Jose Rodríguez-Morató

  • Luca Agnelli

  • Mario Occhipinti

  • Marta Brambilla

  • Teresa Beninato

  • Claudia Proto

  • Sokol Kosta

  • Michele Pio Di Palma

  • Eliana Rulli

  • Stefan Steurer

  • Ronald Simon

  • Michael Willis

  • Giancarlo Pruneri

  • Filippo De Braud

  • Marcello Restelli

  • Enriqueta Felip

  • Nir Peled

  • Alexander T. Pearson

  • Helena Linardou

  • Martin Reck

  • Giuseppe Lo Russo

  • Francesco Trovò

  • Alessandra Laura Giulia Pedrocchi

  • Marina Chiara Garassino

  • September 13, 2026

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Clinical Scorecard: Evaluation of an Explainable AI Decision Support Tool and the Effectiveness of Multimodal Models in Non-Small Cell Lung Cancer Management

At a Glance

CategoryDetail
ConditionNon-Small Cell Lung Cancer (NSCLC)
Key MechanismsImmunotherapy (IO) targeting PD-1, PD-L1, and CTLA-4; multimodal integration of clinical, genomic, and imaging data.
Target PopulationPatients with advanced NSCLC, stage IIIC–IVB.
Care SettingMulticenter clinical research involving retrospective and prospective data.

Key Highlights

  • Long-term benefit of IO occurs in only 20–30% of patients.
  • Primary resistance to IO is seen in 5–20% of patients; secondary resistance in 60–85%.
  • PD-L1 is the only clinically approved biomarker for NSCLC.
  • AI-based decision support systems are being developed to optimize IO selection.
  • Multimodal models have shown improved predictive performance over traditional biomarkers.

Guideline-Based Recommendations

Diagnosis

  • Utilize PD-L1 expression as a biomarker for treatment decisions.

Management

  • Consider IO monotherapy or IO combined with chemotherapy for advanced NSCLC.

Monitoring & Follow-up

  • Assess overall survival (OS), disease control rate (DCR), and clinical benefit rate (CBR) as clinical endpoints.

Risks

  • Monitor for primary and secondary resistance to immunotherapy.

Patient & Prescribing Data

Individuals with advanced NSCLC who have received IO or IO combined with chemotherapy.

The study includes data from 2,396 patients across six clinical centers.

Clinical Best Practices

  • Incorporate multimodal data for personalized treatment selection.
  • Utilize AI-based models for predicting treatment outcomes.

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