Application of artificial intelligence (AI) features of nuclear morphology from BLASST-1 (Bladder Cancer Signal Seeking Trial) of nivolumab, gemcitabine, and cisplatin in patients with MIBC undergoing cystectomy.
Authors: Hammouda Kamal , Gupta Shilpa , Pathak Tilak , Sonpavde Guru P. , Gibb Ewan , Gupta Sumati , Maughan Benjamin L. , Agarwal Neeraj , McGregor Bradley Alexander , Mossanen Matthew , Diaz-Montero C. Marcela , Black Peter C. , Weight Christopher , Mirtti Tuomas , Konety Badrinath R. , Madabhushi Anant 
Published: January 2024

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Background: BLASST-1 is a multi-center phase II trial evaluating neoadjuvant nivolumab (N) with gemcitabine-cisplatin (GC) for patients (pts) with MIBC undergoing radical cystectomy (RC) (NCT03294304). 41 pts with MIBC (cT2-T4a, N≤1, M0) were enrolled between Feb 2018 and June 2019; (cT2N0 90%, cT3N0 7%, cT4N1 3%). Pts received C (70mg/m2) IV on D1, G (1000mg/m2) on D1, D8, and N (360 mg) IV on D8 every 21 days for 4 cycles followed by RC within 8 wks. The primary endpoint was pathologic downstaging (PaR; ≤pT1N0). Safety, Relapse-free survival (RFS), Progression-free survival (PFS) and biomarker analyses were secondary endpoints. PaR rate was 65.8%, the pCR (≤pT is N0) rate was 49% and there were no safety concerns or delays to RC. Morphometric characteristics of the cell nucleus can be used to assess bladder cancer grading and gain insights into cellular functionalities. In this study, we sought to evaluate the ability of the AI model to identify non-responders to neoadjuvant chemo-immunotherapy. in the BLASST-1 cohort based on computerized image features of nuclear morphology and architecture on pre-treatment transurethral resection of bladder tumor (TURBT) tissues. Methods: Of the 41 pts, we had H&E images available for 34 pts, of which 23 had PaR and 11 did not have PaR and these were classified as responder (R) and non-responder (NR) groups. A machine learning model (U-net) was developed and invoked for tumor segmentation on the H&E images from the BLASST-1 cohort. A second machine learning model (HoVer-Net) was employed to segment and classify individual nuclei. A total of 408 features relating to the textural and spatial arrangement of individual cancer nuclei were extracted. The 17 most significant features, identified through the least absolute shrinkage and selection operator, were used to train a Cox regression model to predict the risk of death using 361 MIBC pts from the Cancer Genome Atlas (TCGA). This Cox model was then applied to assign a risk score to pts in BLASST-1, using a threshold learned from TCGA pts, the individual pts in BLASST-1 were assigned as either low-risk or high-risk. Results: The top identified prognostic features described the textural appearance of individual nuclei with more texture. This model accurately predicted PaR in BLASST-1, with an area under a receiver operating characteristic curve of 0.83. Overall, the pts with the same nuclear angle direction within the tumor tissue had a high chance of response to the neoadjuvant chemo-IO combination in the BLASST-1 trial. Conclusions: A computerized AI model relying on nuclear morphologic and architecture features demonstrated prognostic capability in MIBC within the TCGA dataset and predictive capability for the PaR in the BLASST-1 trial. These findings support further validation studies.