BACKGROUND: The tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC). METHODS: This study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(−) or high(+)] with 4 radiomics immune subtypes: 1 (−/−), 2 (+/−), 3 (−/+), and 4 (+/+). We further evaluated the imaging biomarkers’ predictive values on prognosis and immunotherapy response. RESULTS: The developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765–0.773) and myeloid (AUC range: 0.736–0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240–0.761 for LRS; 1.301–4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy ( P < 0.001). Further, a highly heterogeneous outcome on objective response rate was observed in 4 imaging subtypes: 1 (−/−) with 27.3%, 2 (+/−) with 53.3%, 3 (−/+) with 10.2%, and 4 (+/+) with 30.0% ( P < 0.0001). CONCLUSION: The noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.

Multiplexed immunofluorescence (mIF) staining, such as CODEX and MIBI, holds significant clinical value for various fields, such as disease diagnosis, biological research, and drug development. However, these techniques are often hindered by high time and cost requirements. Here we present a Multimodal-Attention-based virtual mIF Staining (MAS) system that utilises a deep learning model to extract potential antibody-related features from dual-modal non-antibody-stained fluorescence imaging, specifically autofluorescence (AF) and DAPI imaging. The MAS system simultaneously generates predictions of mIF with multiple survival-associated biomarkers in gastric cancer using self- and multi-attention learning mechanisms. Experimental results with 180 pathological slides from 94 patients with gastric cancer demonstrate the efficiency and consistent performance of the MAS system in both cancer and noncancer gastric tissues. Furthermore, we showcase the prognostic accuracy of the virtual mIF images of seven gastric cancer related biomarkers, including CD3, CD20, FOXP3, PD1, CD8, CD163, and PD-L1, which is comparable to those obtained from the standard mIF staining. The MAS system rapidly generates reliable multiplexed staining, greatly reducing the cost of mIF and improving clinical workflow. Stanford 2022 HAI Seed Grant; National Institutes of Health 1R01CA256890.

Background: Peritoneal recurrence (PR) is the predominant pattern of relapse after curative-intent surgery in gastric cancer (GC) and indicates a dismal prognosis. Accurate prediction of PR is crucial for patient management and treatment. We aimed to develop a noninvasive imaging biomarker from computed tomography (CT) for PR evaluation, and investigate its associations with prognosis and chemotherapy benefit. Methods: In this multicenter study including five independent cohorts of 2005 GC patients, we extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced CT images. The artificial intelligence algorithms were used to select significant PR-related features, and then integrated into a radiomic imaging signature. And improvements of diagnostic accuracy for PR by clinicians with the signature assistance were quantified. Using Shapley values, we determined the most relevant features and provided explanations to prediction. We further evaluated its predictive performance in prognosis and chemotherapy response. Results: The developed radiomics signature had a consistently high accuracy in predicting PR in the training cohort (area under the curve: 0.732), internal and SYSUCC validation cohorts (0.721 and 0.728). The radiomics signature was the most important feature in Shapley interpretation. The diagnostic accuracy of PR with the radiomics signature assistance was improved by 10.13% to 18.86% for clinicians (P<0.001). Furthermore, it was also applicable in the survival prediction. In multivariable analysis, the radiomics signature remained an independent predictor for PR and prognosis (P<0.001 for all). Importantly, patients with predicting high risk of PR from radiomics signature, could gain survival benefit from adjuvant chemotherapy. By contrast, chemotherapy had no impact on survival for patients with a predicted low risk of PR. Conclusion: The noninvasive and explainable model developed from preoperative CT images could accurately predict PR and chemotherapy benefit in patients with GC, which will allow the optimization of individual decision-making. Export

Current cancer staging methods cannot accurately predict survival outcomes and therapeutic benefits in cancer patients. Digital pathomics, a rapidly evolving field, holds significant potential to revolutionize disease evaluation. This international multicenter study included 2463 patients with pan-gastrointestinal cancer from twelve cohorts of seven cancer centers, of whom 1653 patients were diagnosed with gastric cancer (GC). We proposed a deep learning pathomics signature (DLPS) by integrating information on three scales from the whole slide images (WSIs) of H&E-stained tissue, including pathomic features related to the nucleus, microenvironment, and single-cell spatial distribution. Next, we assessed the predictive accuracy of the DLPS for prognosis, chemotherapy response, and immunotherapy response. Our findings revealed that the DLPS was significantly associated with overall survival in gastric cancer (GC) and pan-gastrointestinal cancers, exhibiting good accuracy with area under the curve values ranging from 0.723 to 0.840 across GC cohorts. Multivariable Cox regression analyses indicated that the DLPS is an independent prognostic factor. The nomogram that integrated the DLPS and TNM stage showed improved performance in predicting cancer prognosis compared to that with TNM stage alone (P < 0.05). Importantly, GC patients with low-DLPS (but not those with high-DLPS) exhibited substantial benefits from adjuvant chemotherapy (P < 0.05). Furthermore, the objective response rate to anti-PD-1 immunotherapy was significantly higher in the low-DLPS group (29.6%) compared to the high-DLPS group (8.3%, P < 0.05). The DLPS has the potential to enhance prognosis assessment and identify patients who are likely to benefit from adjuvant chemotherapy and immunotherapy in pan-gastrointestinal cancers, as well as in other solid tumors.

Purpose:Tertiary lymphoid structures (TLS) are associated with improved survival and enhanced response to anticancer immunotherapy. This study aimed to develop a CT imaging–based approach for noninvasive assessment of TLSs and immunotherapy response.Experimental Design:This study involved 3,155 patients with gastric cancer. TLSs were classified into four stages (absence, Agg, FL-I, and FL-II) according to their maturation based on IHC staining. A CT imaging–based TLS scoring model (ctTLS) was developed to assess TLS status, subsequently classified into four classes (ctTLS-0/1/2/3). We next evaluated the model’s associations with prognosis and immunotherapy response. To enhance the model’s interpretability, we analyzed multiomics data and employed the Shapley value strategy.Results:The ctTLS model achieved high accuracies in predicting TLS status in the internal validation (AUC range, 0.727–0.809) and external validation (0.704–0.807) cohorts. In retrospective and prospective validation cohorts, ctTLSs exhibited significant associations with both disease-free and overall survival (HR range, 0.206–0.634; all P < 0.01). Shapley value analysis highlighted ctTLSs as the strongest predictor of TLS status. Upon analyzing multiomics data, we found that higher ctTLS levels positively correlated with tumor immune activation and apoptosis signaling, while displaying a negative correlation with tumor proliferation and metabolism signaling. Intriguingly, patients with high ctTLSs (but not low ctTLSs) exhibited substantial benefits from immunotherapy (P < 0.0001). The objective response rate of four ctTLS classes was 16.7% in ctTLS-0, 35.5% in ctTLS-1, 45.8% in ctTLS-2, and 53.8% in ctTLS-3.Conclusions:The ctTLS model could noninvasively assess TLS status, enabling improved prognosis evaluation and informed decisions about immunotherapy.