Background We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer. Methods A total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor stroma proportion and the infiltration of immune cells in the stroma region. We further analyzed the correlation between the score and CD3+ T cells density in the stroma region using immunohistochemistry-stained whole-slide images. Survival analysis was performed using the Cox proportional hazard model, and the endpoint of the event was the overall survival. Result Patients were classified into 4-level score groups (score 1–4). A high Deep-immune score was associated with a high level of CD3+ T cells infiltration in the stroma region. In the primary cohort, survival analysis showed a significant difference in 5-year survival rates between score 4 and score 1 groups: 87.4% vs. 58.2% (Hazard ratio for score 4 vs. score 1 0.27, 95% confidence interval 0.15–0.48, P < 0.001). Similar trends were observed in the validation cohort (89.8% vs. 67.0%; 0.31, 0.15–0.62, < 0.001). Stratified analysis showed that the Deep-immune score could distinguish high-risk and low-risk patients in stage II colorectal cancer (P = 0.018). Conclusion The proposed Deep-immune score quantified by artificial intelligence can reflect the immune status of patients with colorectal cancer and is associate with favorable survival. This digital pathology-based finding might advocate change in risk stratification and consequent precision medicine.
Objective: This study aimed to evaluate axillary pathologic complete response (pCR) after neoadjuvant systemic therapy (NST) in clinically node-positive breast cancer (BC) patients based on post-NST multiple-parameter MRI and clinicopathological characteristics. Methods: In this retrospective study, females with clinically node-positive BC who received NST and followed by surgery between January 2017 and September 2021 were included. All axillary lymph nodes (ALNs) on MRI were matched with pathology by ALN markers or sizes. MRI morphological parameters, signal intensity curve (TIC) patterns and apparent diffusion coefficient (ADC) values of post-NST ALNs were measured. The clinicopathological characteristics was also collected and analyzed. Univariable and multivariable logistic regression analyses were performed to evaluate the independent predictors of axillary pCR. Results: Pathologically confirmed 137 non-pCR ALNs in 71 patients and 87 pCR ALNs in 87 patients were included in this study. Cortical thickness, fatty hilum and TIC patterns of ALNs, hormone receptor and human epidermal growth factor receptor 2 (HER2) status were significantly different between the two groups (all, p < 0.05). There was no significant difference for ADC values (p = 0.875). On multivariable analysis, TIC patterns (odds ratio [OR], 2.67, 95% confidence interval [CI]: 1.33, 5.34, p = 0.006), fatty hilum (OR, 2.88, 95% CI:1.39, 5.98, p = 0.004), hormone receptor (OR, 8.40, 95% CI: 2.48, 28.38, p = 0.001) and HER2 status (OR, 8.57, 95% CI: 3.85, 19.08, p < 0.001) were identified as independent predictors associated with axillary pCR. The area under the curve of the multivariate analysis using these predictors was 0.85 (95% CI: 0.79, 0.91). Conclusion: Combining post-NST multiple-parameter MRI and clinicopathological characteristics allowed more accurate identification of BC patients who had received axillary pCR after NST. Advances in knowledge: A combined model incorporated multiple-parameter MRI and clinicopathologic features demonstrated good performance in evaluating axillary pCR preoperatively and noninvasively.
Background In colorectal cancer (CRC), both tumor invasion and immunological analysis at the tumor invasive margin (IM) are significantly associated with patient prognosis, but have traditionally been reported independently. We propose a new scoring system, the TGP-I score, to assess the association and interactions between tumor growth pattern (TGP) and tumor infiltrating lymphocytes at the IM and to predict its prognostic validity for CRC patient stratification. Materials and Methods The types of TGP were assessed in hematoxylin and eosin-stained whole-slide images. The CD3 + T-cells density at the IM was automatically quantified on immunohistochemical-stained slides using a deep learning method. A discovery ( N = 347) and a validation ( N = 132) cohorts were used to evaluate the prognostic value of the TGP-I score for overall survival. Results The TGP-I score 3 (trichotomy) was an independent prognostic factor, with higher TGP-I score 3 associated with worse prognosis in the discovery (unadjusted hazard ratio [HR] for high vs. low 3.62, 95% confidence interval [CI] 2.22–5.90; p < 0.001) and validation cohort (unadjusted HR for high vs. low 5.79, 95% CI 1.84–18.20; p = 0.003). The relative contribution of each parameter to predicting survival was analyzed. The TGP-I score 3 had similar importance compared to tumor-node-metastasis staging (31.2% vs. 32.9%) and was stronger than other clinical parameters. Conclusions This automated workflow and the proposed TGP-I score could further provide accurate prognostic stratification and have potential value for supporting the clinical decision-making of stage I–III CRC patients. Key messages A new scoring system, the TGP-I score, was proposed to assess the association and interactions of TGP and TILs at the tumor invasive margin. TGP-I score could be an independent predictor of prognosis for CRC patients, with higher scores being associated with worse survival. TGP-I score had similar importance compared to tumor-node-metastasis staging and was stronger than other clinical parameters.
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