The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.

Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatment strategies. A significant hurdle for clinical translation of current data-driven deep learning models is lack of interpretability, often attributable to a disconnect from the underlying pathobiology. Here, we present a biology-guided deep learning approach that enables simultaneous prediction of the tumor immune and stromal microenvironment status as well as treatment outcomes from medical images. We validate the model for predicting prognosis of gastric cancer and the benefit from adjuvant chemotherapy in a multi-center international study. Further, the model predicts response to immune checkpoint inhibitors and complements clinically approved biomarkers. Importantly, our model identifies a subset of mismatch repair-deficient tumors that are non-responsive to immunotherapy and may inform the selection of patients for combination treatments.

Objective: To compare neoadjuvant chemotherapy (nCT) with CAPOX alone versus neoadjuvant chemoradiotherapy (nCRT) with capecitabine in locally advanced rectal cancer (LARC) with uninvolved mesorectal fascia (MRF). Background Data: nCRT is associated with higher surgical complications, worse long-term functional outcomes, and questionable survival benefits. Comparatively, nCT alone seems a promising alternative treatment in lower-risk LARC patients with uninvolved MRF. Methods: Patients between June 2014 and October 2020 with LARC within 12 cm from the anal verge and uninvolved MRF were randomly assigned to nCT group with 4 cycles of CAPOX (Oxaliplatin 130 mg/m2 IV day 1 and Capecitabine 1000 mg/m2 twice daily for 14 d. Repeat every 3 wk) or nCRT group with Capecitabine 825 mg/m² twice daily administered orally and concurrently with radiation therapy (50 Gy/25 fractions) for 5 days per week. The primary end point is local-regional recurrence-free survival. Here we reported the results of secondary end points: histopathologic response, surgical events, and toxicity. Results: Of the 663 initially enrolled patients, 589 received the allocated treatment (nCT, n=300; nCRT, n=289). Pathologic complete response rate was 11.0% (95% CI, 7.8-15.3%) in the nCT arm and 13.8% (95% CI, 10.1-18.5%) in the nCRT arm (P=0.33). The downstaging (ypStage 0 to 1) rate was 40.8% (95% CI, 35.1-46.7%) in the nCT arm and 45.6% (95% CI, 39.7-51.7%) in the nCRT arm (P=0.27). nCT was associated with lower perioperative distant metastases rate (0.7% vs. 3.1%, P=0.03) and preventive ileostomy rate (52.2% vs. 63.6%, P=0.008) compared with nCRT. Four patients in the nCT arm received salvage nCRT because of local disease progression after nCT. Two patients in the nCT arm and 5 in the nCRT arm achieved complete clinical response and were treated with a nonsurgical approach. Similar results were observed in subgroup analysis. Conclusions: nCT achieved similar pCR and downstaging rates with lower incidence of perioperative distant metastasis and preventive ileostomy compared with nCRT. CAPOX could be an effective alternative to neoadjuvant therapy in LARC with uninvolved MRF. Long-term follow-up is needed to confirm these results.

The tumor immune microenvironment (TIME) is associated with tumor prognosis and immunotherapy response. Here we develop and validate a CT-based radiomics score (RS) using 2272 gastric cancer (GC) patients to investigate the relationship between the radiomics imaging biomarker and the neutrophil-to-lymphocyte ratio (NLR) in the TIME, including its correlation with prognosis and immunotherapy response in advanced GC. The RS achieves an AUC of 0.795–0.861 in predicting the NLR in the TIME. Notably, the radiomics imaging biomarker is indistinguishable from the IHC-derived NLR status in predicting DFS and OS in each cohort (HR range: 1.694–3.394, P < 0.001). We find the objective responses of a cohort of anti-PD-1 immunotherapy patients is significantly higher in the low-RS group (60.9% and 42.9%) than in the high-RS group (8.1% and 14.3%). The radiomics imaging biomarker is a noninvasive method to evaluate TIME, and may correlate with prognosis and anti PD-1 immunotherapy response in GC patients.

Early prediction of lymph node status after neoadjuvant chemotherapy (NAC) facilitates promptly optimization of treatment strategies. This study aimed to develop and validate a deep learning network (DLN) using baseline computed tomography images to predict lymph node metastasis (LNM) after NAC in patients with locally advanced gastric cancer (LAGC). A total of 1205 LAGC patients were retrospectively recruited from three hospitals between January 2013 and March 2023, constituting a training cohort, an internal validation cohort, and two external validation cohorts. A transformer-based DLN was developed using 3D tumor images to predict LNM after NAC. A clinical model was constructed through multivariate logistic regression analysis as a baseline for subsequent comparisons. The performance of the models was evaluated through discrimination, calibration, and clinical applicability. Furthermore, Kaplan-Meier survival analysis was conducted to assess overall survival (OS) of LAGC patients at two follow-up centers. The DLN outperformed the clinical model and demonstrated a robust performance for predicting LNM in the training and validation cohorts, with areas under the curve (AUCs) of 0.804 (95% confidence interval [CI], 0.752-0.849), 0.748 (95% CI, 0.660-0.830), 0.788 (95% CI, 0.735-0.835), and 0.766 (95% CI, 0.717-0.814), respectively. Decision curve analysis exhibited a high net clinical benefit of the DLN. Moreover, the DLN was significantly associated with the OS of LAGC patients [Center 1: hazard ratio (HR), 1.789, P < 0.001; Center 2:HR, 1.776, P = 0.013]. The transformer-based DLN provides early and effective prediction of LNM and survival outcomes in LAGC patients receiving NAC, with promise to guide individualized therapy. Future prospective multicenter studies are warranted to further validate our model. National Natural Science Foundation of China (NO. 82373432, 82171923, 82202142), Project Funded by China Postdoctoral Science Foundation (NO. 2022M720857), Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (NO. U22A20345), National Science Fund for Distinguished Young Scholars of China (NO. 81925023), Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (NO. 2022B1212010011), High-level Hospital Construction Project (NO. DFJHBF202105), Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (NO. 2024B1515020091).