AI “Pathomics Platform” Selects Immunotherapy, Predicts Outcome for Patients with Metastatic Non-Small Cell Lung Cancer
An interview with:
Rukhmini Bandyopadhyay MD, Postdoctoral Fellow, The University of Texas MD Anderson Cancer Center, Houston, USA.
SAN DIEGO, USA—Pathology slides hold the key to distinguishing precisely which patients with metastatic non-small cell lung cancer can respond well to immune checkpoint inhibition monotherapy from those who need a combination of immunotherapy with chemotherapy, according to a research study discussed at the 2026 Annual Meeting of the American Association for Cancer Research.
In the study, immune signatures recognized by an artificial intelligence-driven “pathomics framework”, were able to assess risk scores to precisely inform survival prediction, according to first author of the study, Rukhmini Bandyopadhyay MD, a postdoctoral fellow at the University of Texas MD Anderson Cancer Center in Houston, USA. She gave more of the details to our reporter Peter Goodwin:
AUDIO JOURNAL OF ONCOLOGY: Rukhmini Bandyopadhyay MD
IN: [GOODWIN] “I am here at the American ………
OUT: ……I’m Peter Goodwin. 10:13”
AACR 2026 Abstract:
Path-IO: A deep learning pathomics framework for personalized immunotherapy selection and outcome prediction in metastatic non-small cell lung cancer
- Bandyopadhyay1, L. Hong1, M. Aldea2, S. Li3, L. Zullo2, F. R. Rojas4, M. B. Saad1, M. C. Marin1, M. Waqas1, J. Zhang1, E. Showkatian1, C. A. Arrechedera1, X. Han1, Y. Kitsel1, S. Ismail1, M. Aminu1, B. Zhu1, C. C. Wu1, B. W. Carter1, J. Chang1, Z. Liao1, M. R. Ghigna2, D. Soldato2, H. T. Tran1, X. Le1, T. Cascone1, B. Zhang1, H. A. Araujo1, M. Altan1, S. Heeke1, D. Jaffray1, D. L. Gibbons1, A. Vaporciyan1, J. Lee1, N. Kalhor1, C. Haymaker1, I. Wistuba5, J. V. Heymach1, Y. Lou3, N. Vokes1, L. M. Solis Soto1, J. Zhang1, J. Wu1;
Institutions:
1UT MD Anderson Cancer Center, Houston, TX,
2Gustave Roussy, Villejuif, France,
3Mayo Clinic, Jacksonville, FL,
4Northwestern University, Chicago, IL,
5Moffitt Cancer Center, Tampa, FL
Background:
Immune checkpoint inhibitors (ICIs) have improved survival in non-small-cell lung cancer (NSCLC), yet only a subset of patients benefits, and biomarkers like PD-L1 remain limited. Here, we introduce a deep learning-based pathomics framework that utilizes routine H&E-stained slides to predict therapeutic response and survival outcomes in ICI-treated metastatic NSCLC.
Methods:
The study included 797 ICI-treated NSCLC patients from MD Anderson, with external validation in 280 patients from Mayo Clinic, Gustave Roussy, and the Phase III ICI-naïve LUSC Lung-MAP S1400I trial receiving nivolumab with or without ipilimumab. Path-IO (Pathology-Driven Immunotherapy Optimization) comprised of four major steps: (1) pathologist-verified tissue classifier segmented WSIs into eight compartments— Background, Bronchi, Immune, Lung, Necrosis, Stroma, Tumor, and Vessel—and was validated on TCGA and CPTAC dataset; (2) a survival prediction module generating patient-level risk scores in the MD Anderson cohort and validated across external datasets; (3) integration of Path-IO risk scores with radiomics and clinical features to improve prognostic accuracy; and (4) biological interpretability analyses correlating Path- IO risk immune contexture from multiplex immunofluorescence and transcriptomic signatures from NanoString profiling.
Results:
Path-IO effectively stratified patients into high and low-risk groups with significant survival differences. In the MD Anderson cohort, it achieved HR = 2.11 (p < 0.001) and HR = 2.51 (p < 0.001) for OS in the discovery and validation sets, and HR = 2.34 (p < 0.001) and HR = 1.87 (p < 0.001) for PFS, respectively. In the multicenter Phase III Lung- MAP S1400I trial, Path-IO achieved HR = 1.78 (p = 0.016) for OS and HR = 2.76 (p = 0.006) for PFS. In external validation, it predicted outcomes in the Gustave Roussy (OS = 1.97, p = 0.003; PFS = 1.51, p = 0.046) and Mayo Clinic (OS = 2.46, p = 0.007; PFS = 2.45, p = 0.027) cohorts. Path-IO outperformed PD-L1 and remained an independent predictor in multivariate analysis (p < 0.001). It enabled data-driven frontline selection between anti-PD-1 monotherapy and chemo-immunotherapy, offering guidance toward more personalized treatment decisions. Integration with radiomics and clinical features further improved predictive accuracy (OS 0.63→0.75; PFS C-index 0.58→0.70), while high Path-IO risk scores correlated with immune-cold phenotypes identified by multiplex immunofluorescence and NanoString transcriptomics.
Conclusions:
Path-IO demonstrates that deep learning models rooted in histopathologic
architecture can generate interpretable and biologically informed survival predictions in NSCLC treated with ICIs. By integrating pathology, radiology, and clinical data, Path-IO provides complementary predictive value beyond established biomarkers such as PD-L1.
PRESS RELEASE:
A Deep Learning Pathomics Platform May Help Predict Response to Immunotherapy in Lung Cancer Patients
SAN DIEGO – A biology-guided artificial intelligence model applied to routine pathology slides accurately predicted outcomes and response to immunotherapy in patients with metastatic non-small cell lung cancer (NSCLC), according to a study presented at the American Association for Cancer Research (AACR) Annual Meeting 2026, held April 17-22.
“Immunotherapy has transformed cancer treatment, but only a subset of patients benefit from it, and predicting who will respond remains challenging,” said presenter Rukhmini Bandyopadhyay, PhD, a postdoctoral fellow at The University of Texas (UT) MD Anderson Cancer Center. “While routine pathology slides contain rich information about the tumor and its surrounding environment, the large amount of complex data can be difficult for human experts to fully quantify.”
Pathomics is an emerging field that applies computational and machine learning methods for high- throughput analysis of digital pathology images to extract and analyze large-scale data related to cell and tissue architecture that can be linked to disease outcomes.
Bandyopadhyay and colleagues developed a pathomics framework based on a deep learning survival prediction model called Pathology-driven Immunotherapy Optimization or Path-IO, which can analyze pathology slide images and study patterns across the tissue to help identify patients who are most likely to benefit from immunotherapy.
“The core idea was to identify specific features, known as niches, within the tumor microenvironment to understand how tumors and the surrounding tissues are organized,” said Bandyopadhyay, adding that the model then combines this information with available imaging and clinical data to estimate whether a patient may have a higher or lower risk of poor outcomes from immunotherapy.
The researchers tested the platform in a study that included 797 immune checkpoint inhibitor-treated NSCLC patients from UT MD Anderson, with external validation in 280 additional patients from Mayo Clinic, Gustave Roussy, and the phase III Lung-MAP S1400I trial in which immunotherapy-naïve patients with lung squamous cell carcinoma (a subtype of NSCLC) were treated with immune checkpoint inhibitors.
Study results showed that the model could reliably stratify patients into higher and lower risk groups with significantly different outcomes. In the UT MD Anderson cohort, patients in the high‐risk group had more than double the risk of death or disease progression compared with patients in the low‐risk group. Comparable results were obtained in the validation datasets.
Model performance was evaluated using the concordance index (C-index), which measures how well each biomarker distinguishes between patients with different outcomes, explained Bandyopadhyay. Notably, Path-IO consistently outperformed PD-L1, the U.S. Food and Drug Administration-validated standard-of-care biomarker for guiding immunotherapy use in NSCLC patients, across both discovery and test cohorts. “PD-L1 alone showed limited prognostic performance, with C-indices of 0.58 for overall survival (OS) and 0.57 for progression-free survival (PFS) in the discovery cohort, declining to 0.50 and 0.51, respectively, in the test cohort. In contrast, Path-IO demonstrated stronger discriminative ability, achieving C-indices of 0.69 for OS and 0.65 for PFS in the discovery cohort and 0.63 for OS and 0.58 for PFS in the test cohort.”
Furthermore, combining pathology-based predictions with radiomics and clinical data further improved the model’s ability to distinguish patient outcomes, with the C-index increasing from 0.58 to 0.70 for PFS and from 0.63 to 0.75 for OS. “These observations highlight the value of integrating multiple sources of information to guide treatment decisions,” said Bandyopadhyay.
Bandyopadhyay explained that, unlike prior pathomics studies, Path-IO is a biology-guided approach that grounds the predictions in tissue structures that are familiar to clinicians to reflect how pathologists naturally interpret tissue.
The model’s predictions correlated with immune profiling and multiplex imaging data because higher risk scores assigned by the model corresponded to phenotypes that are less likely to be sensitive to immunotherapy. “This correlation provides biological evidence for why certain patients may have better or worse outcomes with immunotherapy,” added Bandyopadhyay.
Bandyopadhyay emphasized that, since this approach was designed to be applied to routine pathology slides, if validated as a predictive tool, it could be incorporated into existing clinical workflows without significant expense compared to other emerging data-based technologies.
“This study represents, to our knowledge, the first deep learning-based pathomics biomarker rigorously validated across international real-world cohorts and a phase III randomized clinical trial, directly addressing one of the most urgent unmet needs in precision oncology: reliable patient selection and stratification for immunotherapy,” said Bandyopadhyay.
Limitations of the study, according to Bandyopadhyay, include that the study design is retrospective in nature and, although the results suggest that Path-IO may have predictive value in certain patient subgroups, further investigation is critically needed to go beyond the identification of patients who would benefit from immunotherapy and help predict what type of immunotherapy they can benefit from. “Future directions include prospective validation and the integration of paired, more comprehensive molecular profiling to enhance predictive performance and provide deeper molecular insights,” said Bandyopadhyay.
Rukhmini Bandyopadhyay MD, AACR 2026 Audio Journal of Oncology Text, May 13, 2026
