Neopred: AI-Driven Dual-Phase CT Tool Enhances Preoperative Prediction of Pathological Response in NSCLC

In a landmark advancement poised to reshape the therapeutic landscape for non-small cell lung cancer (NSCLC), researchers led by Professor Jianxing He at the First Affiliated Hospital of Guangzhou Medical University have unveiled an innovative artificial intelligence (AI) model named NeoPred. This cutting-edge system leverages dual-phase computed tomography (CT) imaging alongside clinical data to forecast the major pathological response (MPR) of NSCLC tumors to neoadjuvant chemo-immunotherapy well before surgical intervention, integrating AI’s power into one of the most critical junctures of lung cancer care.

Neoadjuvant chemo-immunotherapy has emerged as a frontline approach to increase the resectability and overall prognosis of patients with early-stage or locally advanced NSCLC. Despite its promise, the evaluation of treatment efficacy conventionally relies on postoperative pathological assessments, a process that can only be performed after tumor resection. This delay limits clinicians’ ability to dynamically refine therapeutic strategies during the neoadjuvant window and extends patient exposure to potentially ineffective regimens. The development of NeoPred targets this clinical impasse by enabling real-time, non-invasive predictions of MPR to guide surgical and systemic treatment decisions more proactively.

NeoPred’s unique strength lies in its utilization of dual-phase CT imaging — capturing tumor morphology and response dynamics both before the initiation of therapy and just prior to surgery. This dual temporal perspective allows the AI model to quantify subtle morphological changes induced by chemo-immunotherapy that often escape human visual assessment. Beyond imaging, the model incorporates a multimodal framework by integrating critical clinical parameters such as age, sex, body mass index, and tumor staging. Together, these data streams are fused within advanced 3D convolutional neural networks, forming a robust predictive architecture that tailors the evaluation to each patient’s complex clinical profile.

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The study underpinning NeoPred is remarkable in scale and scope. Drawing on a diverse cohort of 509 NSCLC patients across four distinguished thoracic oncology centers, the research team adopted a rigorous methodology involving retrospective model training and validation on 459 cases, supplemented by prospective real-world testing on 50 additional patients. To further validate their findings externally, 59 independent cases from collaborating institutions served as a test set, reinforcing the model’s generalizability and clinical applicability across heterogeneous populations and imaging protocols.

Critically, NeoPred demonstrated formidable performance metrics throughout its evaluations. In the external validation cohort, the AI system achieved an area under the receiver operating characteristic curve (AUC) of 0.772 using imaging data alone; this precision improved with the inclusion of clinical variables, elevating the AUC to 0.787. These results underscore the added value of integrating multimodal information beyond traditional radiomics. Moreover, the model’s predictive power shone in prospective clinical application, where NeoPred surpassed expert thoracic surgeons’ interpretation accuracy, with an AUC of 0.760 compared to the human benchmark of 0.720.

One of the most compelling breakthroughs emerged from the study’s investigation into “stable disease” (SD) cases classified according to the RECIST criteria. While these patients exhibit limited apparent tumor shrinkage post-therapy, NeoPred successfully uncovered underlying major pathological responders within this group, achieving AUCs of 0.742 in external datasets and an even more striking 0.833 in prospective cases. This capability to detect “pseudo-stable” responders highlights the model’s sensitivity to nuanced morphological and dynamic tumor transformations that traditional radiological assessments might overlook, offering a critical edge in personalized treatment planning.

NeoPred’s clinical implications extend beyond mere prognostication. By delivering early and reliable predictions of pathological response one to two weeks before surgery, the model facilitates evidence-based adjustments in perioperative strategies, potentially sparing patients from unnecessary surgical morbidity or enabling timely alterations in systemic therapy. The integration of AI-generated quantitative metrics into multidisciplinary team discussions promises to streamline workflows, promote objective risk stratification, and enhance collaborative decision-making—a transformational step toward precision oncology in thoracic surgery.

This breakthrough AI initiative also forms an integral component of a broader ecosystem of computational tools masterminded by Prof. He’s team, designed to address the multifaceted challenges of lung cancer management. Technologies span early detection platforms combining cell-free DNA methylation assays and low-dose CT scans, advanced proteomics for plasma biomarker identification, natural language processing algorithms for electronic health record mining, and sophisticated deep learning models predicting gene mutations directly from histopathology slides. Each innovation complements NeoPred’s intent by fortifying the continuum from screening to therapeutic response monitoring.

Technological sophistication characterizes the NeoPred model’s architecture. It employs dual 3D convolutional neural networks independently trained on pre-treatment and pre-surgery CT imagery, facilitating dedicated feature extraction at each time point. A fusion mechanism consolidates outputs alongside clinical indicators, enhancing predictive cohesion. Notably, this workflow reflects a trend toward multimodal AI solutions that transcend the limits of isolated data sources, fostering holistic patient modeling and dynamic disease surveillance.

Importantly, the study not only quantified AI’s diagnostic superiority but also examined human-AI collaboration paradigms. Nine expert thoracic surgeons were asked to interpret CT scans initially without and subsequently with access to NeoPred’s predictive heatmaps. Post-exposure to AI insights, surgeons’ diagnostic accuracy surged to 82%, with an impressive AUC elevation to 0.829. This synergy exemplifies how AI tools can augment clinical expertise rather than supplant it, cultivating a collaborative interface that leverages machine precision and human judgment to optimize outcomes.

Beyond the realm of neoadjuvant therapy evaluation, NeoPred’s methodology portends future adaptability to other oncologic contexts where preoperative response assessment remains elusive. Its “blind spot complementarity,” as shown by identifying responders hidden within stable radiological presentations, positions the model as a prototype for AI-mediated phenotyping capable of unraveling tumor heterogeneity and microenvironmental complexities embedded within imaging phenotypes.

The integration of federated learning platforms within the ecosystem, such as the CAIMEN system fostered across over 40 institutions, underscores the team’s commitment to data security and collaborative model refinement without compromising patient privacy. This decentralized approach to AI training not only expands the diversity of representative data but also propels diagnostic accuracy across geographic and institutional boundaries, promising equitable precision medicine deployment.

As lung cancer remains the leading cause of cancer mortality worldwide, innovations like NeoPred usher in a new era where AI-driven insights guide therapeutic timelines, refine surgical candidacy, and personalize treatment trajectories with unprecedented granularity and timeliness. The convergence of multimodal imaging, deep learning, and clinical data modeling exemplified by NeoPred reflects the burgeoning potential of AI to revolutionize oncologic care, bringing hope for improved survival and quality of life to patients confronting NSCLC.

In sum, NeoPred exemplifies a paradigm shift in thoracic oncology, converging computational power and clinical acumen to anticipate tumor response before surgery, optimize treatment efficacy, and ultimately enhance patient-centered care. The pioneering work by Professor Jianxing He and colleagues marks a significant stride in operationalizing AI as a tangible, high-impact tool within modern cancer management, heralding broader adoption and continual innovation in this dynamic field.

Subject of Research: People

Article Title: NeoPred: dual-phase CT AI forecasts pathologic response to neoadjuvant chemo-immunotherapy in NSCLC

News Publication Date: 31-May-2025

Web References: http://dx.doi.org/10.1136/jitc-2025-011773

References:
[1] Zheng J, Yan Z, Wang R, et al. NeoPred: dual-phase CT AI forecasts pathologic response to neoadjuvant chemo-immunotherapy in NSCLC. J Immunother Cancer. 2025;13(5):e011773. doi:10.1136/jitc-2025-011773.

Keywords: Lung cancer, Immunotherapy, Adjuvants

Tags: AI-driven lung cancer predictionartificial intelligence in cancer treatmentclinical data integration in oncologydual-phase CT imaging for NSCLCearly-stage lung cancer prognosisenhancing surgical decision-making in NSCLCinnovative technology in cancer therapymajor pathological response forecastingneoadjuvant chemo-immunotherapy effectivenessnon-invasive tumor assessment methodspreoperative prediction tools for lung cancertransformative advancements in cancer care