Citations 2
Bridging the clinical gap: Confidence informed IDH prediction in brain gliomas using MRI and deep learning
Yogananda, Chandan Ganesh Bangalore , Truong, Nghi Cong Dung , Wagner, Benjamin , Bowerman, Jason, Reddy, Divya D. , Holcomb, James M. , Saadat, Niloufar , Hatanpaa, Kimmo J. , Lee, Matthew , Bruce, Richard J. , Madhuranthakam, Ananth J. , Bangalore Yogananda, Chandan Ganesh , Jain, Rajan , Xi, Yin , Patel, Toral , Fei, Baowei , Jain, Rajan , Pinho, Marco C. , Maldjian, Joseph A.
Neuro-Oncology Advances | August 20, 2025 | Volume: 7 | Issue: 1 | Pages: No page available
Abstract Background The isocitrate dehydrogenase (IDH) mutation status is a key molecular marker in diagnosing and treating brain tumors. Currently, it is determined via invasive tissue biopsy. Recent advances in deep learning (DL) have offered promising non-invasive alternatives for determining IDH status. However, their clinical translation is hindered by a significant gap between DL predictions and their clinical applicability. The limited transparency of many DL-networks and inadequate evaluation metrics hinders trust and adoption, as clinicians require clear and validated insights for determining IDH status. These challenges highlight the need for robust validation and measures of predictive reliability to make DL-predictions clinically actionable. Methods We developed a unique approach for non-invasive prediction of IDH status using MRI. We combine a voxel-wise-segmentation network(MC-net) with Bayesian logistic regression (BLR) to provide an IDH status and estimate confidence scores. We utilized a comprehensive dataset of 2,481 glioma cases from eight institutions. Results Our framework(MC-net + BLR) demonstrated robust performance achieving 96.4% and 95.1% classification accuracies on diverse databases, with an AUC of 0.98. The BLR was implemented exclusively on held-out test data, ensuring that the derived confidence scores are independent of the training or validation phases. The derived confidence scores showed a low Brier score of 0.0125, highlighting its superior calibration and uncertainty quantification. Conclusion The developed framework provides an IDH status and a confidence score, offering clinicians an additional layer of assurance in prediction reliability. It bridges the gap between high-performing DL models and their clinical applicability by addressing the challenges in prediction reliability. Our framework is a significant advancement in non-invasive determination of IDH-status and confidence-informed therapeutic decision-making in neuro-oncology.
Integrated Molecular and Clinical Characterization of Pulmonary Large Cell Neuroendocrine Carcinoma
Chiang, Anne C. , Nassar, Amin H. , Kim, Chul , Farhat, Elias Bou , Matteson, Kelsey, Lau, Shun‐Fat , Gallegos, Jesús Antonio Ocejo , Ardeshir‐Larijani, Fatemeh , Leal, Ticiana, Alam, Sumaye, Gray, Jhanelle E. , Hicks, J. Kevin , Kaldas, David , Baena, Javier, Berjaga, María Zurera, Grohé, Christian , Leuders, Heike, Mingo, Emanuele Claudio , Pancirer, Danny, Cheung, Justin M., Lin, Jessica, Watson, Alexander, Camidge, D. Ross , Parikh, Kaushal , Aggarwal, Vanya , Ahmed, Murtaza, Zhang, Jun , Nagasaka, Misako , Awosika, Nichola, Alhamad, Khaled, Zaman, Unaiza, Gupta, Divya , Lau, Chelsea, Khan, Hina, Liauw, Justin , Brown, Tyiesha , Mosteiro, Miguel , Lopes, Gilberto , Patrick, C. , Young, Lauren K. , Aljumaily, Raid, Mirza, Haris , TAKEBE, Y., Baena, Javier , Nassar, Amin H. , Ocejo Gallegos, Jesus Antonio , Bou Farhat, Elias , Espinar, Javier Baena , Adeyelu, Tolulope Tosin , Abushukair, Hassan , Rakaee, Mehrdad , Takabe, Yamato J. , Ramalingam, Suresh S. , Alam, Sumaiya, Nana, Frank Aboubakar , Citarella, Fabrizio , Cortellini, Alessio , Das, Millie , Ellis-Caleo, Timothy John , Linehan, William Marston , Sridhar, Arthi , Crowley, Fionnuala , Marron, Thomas , Sankar, K. Nathan , Kawtharany, Hassan , Owen, Dwight H , Li, Mingjia , Pinato, David J. , Puri, Sonam , Velázquez, Ana I. , Moliner, Laura , Rocha, Pedro , Evans, Mark G. , Vanderwalde, Ari M. , Elliott, Andrew , Nieva, Jorgé , Patrick, C. , Borghaei, Hossein , Lee, Matthew , Kwiatkowski, David J. , Herbst, Roy S. , Flavell, Richard A. , Naqash, Abdul Rafeh
No container title available | May 16, 2025 | Volume: No volume available | Issue: No issue available | Pages: No page available
Abstract Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive lung tumor marked by significant molecular heterogeneity. In a study of 590 patients across two independent cohorts, we observed comparable overall survival across treatment regimens (chemotherapy, chemoimmunotherapy, immunotherapy) without unexpected adverse events. Genomic analysis identified distinct NSCLC-like (KEAP1, KRAS, STK11 mutations) and SCLC-like (RB1, TP53 mutations) LCNEC subtypes, with 80% aligning with SCLC transcriptional profiles. Serial sampling revealed stable mutational but shifting transcriptomic landscapes over time. NSCLC-like LCNECs showed elevated FGL-1 (a LAG-3 ligand) and SPINK1 expression, while SCLC-like subtypes expressed higher levels of DLL3. Immunofluorescence confirmed FGL-1 in NSCLC-like LCNECs, and H&E slide analyses indicated fewer tumor-infiltrating lymphocytes in LCNECs versus other lung cancers. These findings highlight LCNEC’s distinct immunogenomic profile, supporting future investigations into LAG-3, SPINK1, and DLL3-targeted therapies.