Prognostic value and clinicopathological correlation of the tumor regression grade in neoadjuvant chemotherapy for gastric adenocarcinoma: a retrospective cohort study

A novel ypN-TRG staging system for gastric cancer patients after neoadjuvant therapy based on the metro-ticket paradigm: a multicenter and large sample retrospective analysis

BACKGROUND

Conventional ypT category evaluates the depth of invasion after neoadjuvant therapy (NAT) for gastric cancer (GC) and has limited prognostic value. Tumor regression grade (TRG) measures the extent of tumor response to treatment, and when combined with the ypN category, it may enhance the prediction of patient outcomes. This study aims to develop a new staging system by integrating TRG and ypN category to better evaluate the prognosis of GC patients receiving NAT.

METHODS

This retrospective analysis included 962 patients who underwent radical gastrectomy after NAT, with 513 in the development cohort (from one center) and 449 in the external validation cohort (from five centers). The ypN-TRG staging system was established by calculating the distance from the origin on a cartesian plane incorporating the ypN (x-axis) stage and TRG (y-axis) grade, and five sub-stages were delineated.

RESULTS

In the development cohort, 3-year overall survival rates according to ypN-TRG stage I, IIA, IIB, IIIA, IIB were 87.6%, 80.2%, 70.7%, 47.3%, 21.5%, p < 0.01. Compared with ypTNM, the ypN-TRG staging system performed better in terms of the prognostic discrimination power (C-index), goodness-of-fit (AIC, BIC), model improvement (NRI, IDI), and model stability (time-AUC). Multivariate Cox regression analysis confirmed the superiority of ypN-TRG over ypTNM staging. In the external validation cohort, ypN-TRG staging was a better predictor of OS and DFS in patients with GC.

CONCLUSIONS

The ypN-TRG staging system is superior to the AJCC eighth edition ypTNM staging system in accurately assessing the prognosis of patients with GC after NAT.

Predicting neoadjuvant chemotherapy response in locally advanced gastric cancer using a machine learning model combining radiomics and clinical biomarkers

Rationale and objectives

Neoadjuvant chemotherapy (NAC) is a promising therapeutic strategy for managing locally advanced gastric cancer (LAGC), aiming to reduce tumor burden, enhance resection rates, and improve clinical outcomes. Due to variability in patient responses, the objective of this study was to enhance the prediction of NAC tumor regression grade (TRG) in patients with LAGC by integrating radiomic features with clinical biomarkers through machine learning (ML) approaches.

Materials and methods

We analyzed a cohort of 255 patients with LAGC who underwent NAC prior to surgical resection at the Affiliated Cancer Hospital of Guangxi Medical University. Among these patients, 57 (22.4%) were classified as responders (TRG 0-1), and 198 (77.6%) were identified as non-responders (TRG 2-3). The cohort was divided into a training set (n = 178) and a validation set (n = 77) in a 7:3 ratio. Pre-treatment portal venous-phase computed tomography scans were used to extract 1130 radiomic features via the OnekeyAI platform software. Through feature engineering, we generated a radiomics score (rad score) by linearly combining these features. A variety of ML algorithms were applied to integrate the rad score with clinical biomarkers, resulting in the construction of a hybrid model. The model's diagnostic performance was evaluated using receiver operating characteristic curves and the area under the curve (AUC).

Results

Among the ML models tested, the random forest (RF) model performed best when both the rad score and clinical biomarkers were used as input features, leading to our hybrid model development. This hybrid model (AUC = 0.814) outperformed the radiomics (AUC = 0.755) and clinical (AUC = 0.682) models.

Conclusion

A RF-based hybrid model was developed by integrating radiomics and clinical biomarkers to predict NAC response in patients with LAGC undergoing surgical resection, providing personalized treatment insights.

Multiomics reveals tumor microenvironment remodeling in locally advanced gastric and gastroesophageal junction cancer following neoadjuvant immunotherapy and chemotherapy

BACKGROUND

Perioperative chemotherapy is the standard of care for patients with locally advanced gastric and gastroesophageal junction cancer. Recent evidence demonstrated the addition of programmed cell death protein 1 (PD-1) inhibitors enhanced therapeutic efficacy. However, the mechanisms of response and resistance remain largely undefined. A detailed multiomic investigation is essential to elucidate these mechanisms.

METHODS

We performed whole-exome sequencing, whole-transcriptome sequencing, multiplex immunofluorescence and single-cell RNA sequencing on matched pretreatment and post-treatment samples from 30 patients enrolled in an investigator-initiated Phase 2 clinical trial (NCT04908566). All patients received neoadjuvant PD-1 inhibitors in combination with chemotherapy. A major pathologic response (MPR) was defined as the presence of no more than 10% residual viable tumor cells following treatment.

RESULTS

Before treatment, the positive ratio of CD3+T cells in both the tumor parenchyma and stroma was significantly higher in the non-MPR group compared with the MPR group (p=0.042 and p=0.013, respectively). Least absolute shrinkage and selection operator regression was employed for feature gene selection and 13 genes were ultimately used to construct a predictive model for identifying MPR after surgery. The model exhibited a perfect area under curve (AUC) of 1.000 (95% CI: 1.000 to 1.000, p<0.001). Post-treatment analysis revealed a significant increase in CD3+T cells, CD8+T cells and NK cells in the tumor stroma of MPR patients. In the tumor parenchyma, aside from a marked increase in CD8+T cells and NK cells, a notable reduction in macrophage was also observed (all p<0.05). Importantly, forkheadbox protein 3 (FOXP3), the principal marker for regulatory T cells (Treg) cells, showed a significant decrease during treatment in MPR patients. FOXP3 expression in the non-MPR group was significantly higher than in the MPR group (p=0.0056) after treatment. Furthermore, single-cell RNA sequencing analysis confirmed that nearly all Treg cells were derived from the non-MPR group.

CONCLUSIONS

Our study highlights the critical role of dynamic changes within the tumor immune microenvironment in predicting the efficacy of neoadjuvant combined immunochemotherapy. We examined the disparities between MPR/non-MPR groups, shedding light on potential mechanisms of immune response and suppression. In addition to bolstering cytotoxic immune responses, specifically targeting Treg cells may be crucial for enhancing treatment outcomes.

Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients

PURPOSE

Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients.

METHODS

This multi-center study retrospectively included 322 patients diagnosed with gastric cancer from January 2013 to June 2023 at two hospitals. Handcrafted radiomics technique and the EfficientNet V2 neural network were applied to arterial, portal venous, and delayed phase CT images to extract two-dimensional handcrafted and deep learning features. A nomogram model was built by integrating the handcrafted signature, the deep learning signature, with clinical features. Discriminative ability was assessed using the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve. Model fitting was evaluated using calibration curves, and clinical utility was assessed through decision curve analysis (DCA).

RESULTS

The nomogram exhibited excellent performance. The area under the ROC curve (AUC) was 0.848 [95% confidence interval (CI), 0.793-0.893)], 0.802 (95% CI 0.688-0.889), and 0.751 (95% CI 0.652-0.833) for the training, internal validation, and external validation sets, respectively. The AUCs of the P-R curves were 0.838 (95% CI 0.756-0.895), 0.541 (95% CI 0.329-0.740), and 0.556 (95% CI 0.376-0.722) for the corresponding sets. The nomogram outperformed the clinical model and handcrafted signature across all sets (all P < 0.05). The nomogram model demonstrated good calibration and provided greater net benefit within the relevant threshold range compared to other models.

CONCLUSION

This study created a deep learning nomogram using CECT images and clinical data to predict NAC response in LAGC patients undergoing surgical resection, offering personalized treatment insights.

Prognostic value of tumor regression grade (TRG) after oncological gastrectomy for gastric cancer

PURPOSE

Perioperative chemotherapy combined with surgical resection represent the gold standard in the treatment of locally advanced gastric cancer. The Mandard tumor regression score (TRG) is widely used to evaluate pathological response to neoadjuvant treatment. The aim of this study was to assess the prognostic value of TRG in terms of overall survival (OS) and disease-free (DFS).

METHODS

Retrospective analysis of all consecutive patients who underwent oncological gastrectomy after neoadjuvant chemotherapy from January 2007 to December 2019 for gastric adenocarcinoma was performed. Based on their TRG status they were categorized into two groups: good responders (TRG 1-2) and poor responders (TRG 3-5). Subsequent multivariable analyses were conducted.

RESULTS

Seventy-four patients were included, whereby 15 (20.3%) were TRG 1-2. Neoadjuvant regimens for TRG 1-2 vs. TRG 3-5 were similar: MAGIC (53% vs. 39%), FLOT (40% vs. 36%), FOLFOX (7% vs. 15%, p = 0.462). Histologic types according to Lauren classification for TRG 1-2 vs. TRG 3-5 were: 13% vs. 29% intestinal, 53% vs. 44% diffuse and 34% vs. 27% indeterminate (p = 0.326). TRG 1-2 group exhibited significantly less advanced ypT (46% vs. 10%, p = 0.001) and ypN stages (66% vs. 37%, p = 0.008), alongside a diminished recurrence rate (20% vs. 42%, p = 0.111). The 3-year DFS was significantly better in this group (81% vs. 47%, p = 0.041) whereas the disparity in three-year OS (92% vs. 55%, p = 0.054) did not attain statistical significance.

CONCLUSIONS

TRG 1-2 was associated with less advanced ypT and ypN stage and better DFS compared to TRG 3-5 patients, without a significant impact on OS.

Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

BACKGROUND

Neoadjuvant immunochemotherapy (NICT) plus esophagectomy has emerged as a promising treatment option for locally advanced esophageal squamous cell carcinoma (LA-ESCC). Pathologic complete response (pCR) is a key indicator associated with great efficacy and overall survival (OS). However, there are insufficient indicators for the reliable assessment of pCR.

METHODS

192 patients with LA-ESCC treated with NICT from December 2019 to October 2023 were recruited. According to pCR status, patients were categorized into pCR group (22.92%) and non-pCR group (77.08%). Radiological features of pretreatment and preoperative CT images were extracted. Logistic and COX regressions were trained to predict pathological response and prognosis, respectively.

RESULTS

Four of the selected radiological features were combined to construct an ESCC preoperative imaging score (ECPI-Score). Logistic models revealed independent associations of ECPI-Score and vascular sign with pCR, with AUC of 0.918 in the training set and 0.862 in the validation set, respectively. After grouping by ECPI-Score, a higher proportion of pCR was observed among the high-ECPI group and negative vascular sign. Kaplan Meier analysis demonstrated that recurrence-free survival (RFS) with negative vascular sign was significantly better than those with positive (P = 0.038), but not for OS (P = 0.310).

CONCLUSIONS

This study demonstrates dynamic radiological features are independent predictors of pCR for LA-ESCC treated with NICT. It will guide clinicians to make accurate treatment plans.

Conversion surgery for gastric remnant cancer with liver metastasis after nivolumab combination chemotherapy achieving pathological complete response: a case report and literature review

BACKGROUND

Nivolumab combination chemotherapy has recently emerged as a potential first-line treatment for patients with unresectable or metastatic gastric cancer (GC). Further research has indicated that R0 resection by conversion surgery could be an effective treatment strategy to improve overall survival. However, there have been limited reports on the successful application of conversion surgery following combination chemotherapy achieving pathological complete response (pCR) in cases of advanced gastric remnant cancer with liver metastasis. Here, we present a case of long-term survival in a patient who underwent this treatment.

CASE PRESENTATION

A 54-year-old man was initially referred to our department for treatment of stage III (cT3N1M0) gastric cancer where he underwent laparoscopic distal gastrectomy and D2 lymph node dissection. After a year of uneventful follow-up, the patient was diagnosed with a tumor in the gastric remnant combined with liver metastasis, resulting in a diagnosis of stage IV (cT3N0M1) gastric remnant cancer. Subsequently, the patient was treated with four cycles of TS-1, Oxaliplatin, and Nivolumab as the first-line regimen. Remarkably, both the remnant tumor and liver metastasis exhibited significant shrinkage, and no new lesions were found. Given this response, conversion surgery was performed to achieve complete resection of the remnant gastric cancer and liver metastasis, followed by laparoscopic remnant gastrectomy and partial hepatectomy. Pathological examination revealed the absence of residual carcinoma cells and lymph node metastases. Postoperatively, the patient was treated with adjuvant chemotherapy with S-1 for 1 year, and survived without recurrence for 18 months after conversion surgery.

CONCLUSIONS

Nivolumab combination chemotherapy shows promise as a clinically beneficial treatment approach for gastric remnant cancer with liver metastasis, particularly when pCR can be achieved following conversion surgery.

Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer

BACKGROUND

We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC).

METHODS

We prospectively enrolled 112 patients with LAGC who received NACT from January 2021 to August 2022. After applying the inclusion and exclusion criteria, 98 patients were randomized 7:3 to the training cohort (n = 68) and validation cohort (n = 30). We established and compared three radiomics signatures based on three phases of CT images before and after NACT, namely radiomics-baseline, radiomics-delta, and radiomics-restage. Then, we developed a clinical model, DL model, and a nomogram to predict the response of LAGC after NACT. We evaluated the predictive accuracy and clinical validity of each model using the receiver operating characteristic curve and decision curve analysis, respectively.

RESULTS

The radiomics-delta signature was the best predictor among the three radiomics signatures. So, we developed and validated a DL delta radiomics nomogram (DLDRN). In the validation cohort, the DLDRN produced an area under the receiver operating curve of 0.94 (95% confidence interval, 0.82-0.96) and demonstrated adequate differentiation of good response to NACT. Furthermore, the DLDRN significantly outperformed the clinical model and DL model (p < 0.001). The clinical utility of the DLDRN was confirmed through decision curve analysis.

CONCLUSIONS

In patients with LAGC, the DLDRN effectively predicted a therapeutic response in metastatic lymph nodes, which could provide valuable information for individualized treatment.

The role of computed tomography features in assessing response to neoadjuvant chemotherapy in locally advanced gastric cancer

OBJECTIVE

To compare the computed tomography (CT) images of patients with locally advanced gastric cancer (GC) before and after neoadjuvant chemotherapy (NAC) in order to identify CT features that could predict pathological response to NAC.

METHODS

We included patients with locally advanced GC who underwent gastrectomy after NAC from September 2016 to September 2021. We retrieved and collected the patients' clinicopathological characteristics and CT images before and after NAC. We analyzed CT features that could differentiate responders from non-responders and established a logistic regression equation based on these features.

RESULTS

We included 97 patients (69 [71.1%] men; median [range] age, 60 [26-75] years) in this study, including 66 (68.0%) responders and 31 (32.0%) non-responders. No clinicopathological variable prior to treatment was significantly associated with pathological response. Out of 16 features, three features (ratio of tumor thickness reduction, ratio of reduction of primary tumor attenuation in arterial phase, and ratio of reduction of largest lymph node attenuation in venous phase) on logistic regression analysis were used to establish a regression equation that demonstrated good discrimination performance in predicting pathological response (area under receiver operating characteristic curve 0.955; 95% CI, 0.911-0.998).

CONCLUSION

Logistic regression equation based on three CT features can help predict the pathological response of patients with locally advanced GC to NAC.

Concurrent clinical and pathological response predicts favorable prognosis of patients with gastric cancer after neoadjuvant therapy: a real-world study

BACKGROUND

Response of locally advanced gastric cancer (LAGC) to neoadjuvant therapy (NAT) may be associated with prognosis, but which of the clinical or pathological evaluation can accurately predict a favorable prognosis is still controversial. This study aims to compare the effect of clinical and pathological response on the prognosis of patients with gastric cancer.

METHODS

This study retrospectively analyzed LAGC patients who underwent NAT followed by surgery in the China National Cancer Center from January 2004 to January 2021. Clinical and pathological responses after NAT were evaluated using RECIST 1.1 and Mandard tumor regression grade system (TRG) respectively. Complete response (CR) and partial response (PR) assessed by computed tomography were regarded as clinical response. For histopathology regression assessment, response was defined as Mandard 1, 2, 3 and non-response as Mandard 4, 5. Furthermore, we combined clinical and pathological evaluation results into a variable termed "comprehensive assessment" and divided it into four groups based on the presence or absence of response (concurrent response, only clinical response, only pathological response, both non-response). The association between the prognosis and clinicopathological factors was assessed in univariate and multivariate Cox regression analysis.

RESULTS

In total, 238 of 1073 patients were included in the study after screening. The postoperative pathological response rate and clinical response rate were 50.84% (121/238) and 39.92% (95/238), respectively. 154 patients got consistent results in clinical and pathological evaluation (66 were concurrent response and 88 were both non-response), while the other 84 patients did not. The kappa value was 0.297(p < 0.001), which showed poor consistency. Multivariate Cox regression analysis revealed that comprehensive assessment (P = 0.03), clinical N stage(P < 0.001), vascular or lymphatic invasion (VOLI) (HR 2.745, P < 0.001), and pre-CA724(HR 1.577, P = 0.047) were independent factors for overall survival in patients with gastric cancer. Among four groups in the comprehensive assessment, concurrent response had significantly better survival (median OS: 103.5 months) than the other groups (P = 0.008).

CONCLUSION

Concurrent clinical and pathological response might predict a favorable prognosis of patients with gastric cancer after neoadjuvant therapy, further validation is needed in prospective clinical trials with larger samples.

Defining a Nomogram for Predicting Early Recurrence in Gastric Cancer Patients After Neoadjuvant Chemotherapy and Radical Gastrectomy

PURPOSE

To define and predict early recurrence (ER) in patients with gastric cancer (GC) who underwent radical gastrectomy after neoadjuvant chemotherapy (NAC).

METHODS

The present study included 573 patients who underwent NAC followed by curative resection for GC between January 2014 and December 2019. The patients were randomly divided into the training (n = 382) and validation (n = 191) cohorts in a 2:1 ratio. The optimal cut-off value of recurrence-free survival for defining ER was determined based on post-recurrence survival (PRS). Risk factors for ER were identified by logistic regression. A nomogram was further constructed and evaluated.

RESULTS

The optimal cut-off value for defining ER was 12 months. Overall, 136 patients (23.7%) experienced ER and had significantly shorter median PRS (4 vs. 13 months, P < 0.001). In the training cohort, factors independently associated with ER included age (P = 0.026), Lauren classification (P < 0.001), preoperative carcinoembryonic antigen (P = 0.029), ypN staging (P < 0.001), major pathological regression (P = 0.004), and postoperative complications (P < 0.001). A nomogram integrating these factors exhibited higher predictive accuracy than the ypTNM stage alone in both the training and validation cohorts. Moreover, the nomogram enabled significant risk stratification in both cohorts; only the high-risk patients could benefit from adjuvant chemotherapy (ER rate: 53.9% vs. 85.7%, P = 0.007).

CONCLUSION

A nomogram involving preoperative factors can accurately predict the risk of ER and guide individualized treatment strategies for GC patients following NAC, which may assist in clinical decision-making.

Deep learning radio-clinical signatures for predicting neoadjuvant chemotherapy response and prognosis from pretreatment CT images of locally advanced gastric cancer patients

BACKGROUND

Early noninvasive screening of patients who would benefit from neoadjuvant chemotherapy (NCT) is essential for personalized treatment of locally advanced gastric cancer (LAGC). The aim of this study was to identify radio-clinical signatures from pretreatment oversampled computed tomography (CT) images to predict the response to NCT and prognosis of LAGC patients.

METHODS

LAGC patients were retrospectively recruited from six hospitals from January 2008 to December 2021. An SE-ResNet50-based chemotherapy response prediction system was developed from pretreatment CT images preprocessed with an imaging oversampling method (i.e. DeepSMOTE). Then, the deep learning (DL) signature and clinic-based features were fed into the deep learning radio-clinical signature (DLCS). The predictive performance of the model was evaluated based on discrimination, calibration, and clinical usefulness. An additional model was built to predict overall survival (OS) and explore the survival benefit of the proposed DL signature and clinicopathological characteristics.

RESULTS

A total of 1060 LAGC patients were recruited from six hospitals; the training cohort (TC) and internal validation cohort (IVC) patients were randomly selected from center I. An external validation cohort (EVC) of 265 patients from five other centers was also included. The DLCS exhibited excellent performance in predicting the response to NCT in the IVC [area under the curve (AUC), 0.86] and EVC (AUC, 0.82), with good calibration in all cohorts ( P >0.05). Moreover, the DLCS model outperformed the clinical model ( P <0.05). Additionally, we found that the DL signature could serve as an independent factor for prognosis [hazard ratio (HR), 0.828, P =0.004]. The concordance index (C-index), integrated area under the time-dependent ROC curve (iAUC), and integrated Brier score (IBS) for the OS model were 0.64, 1.24, and 0.71 in the test set.

CONCLUSION

The authors proposed a DLCS model that combined imaging features with clinical risk factors to accurately predict tumor response and identify the risk of OS in LAGC patients prior to NCT, which can then be used to guide personalized treatment plans with the help of computerized tumor-level characterization.

Tumor Regression Grade and Overall Survival following Gastrectomy with Preoperative Therapy for Gastric Cancer

BACKGROUND

Pre-/perioperative chemotherapy is well-established for management of locoregional gastric cancer (LRGC). The American Joint Committee on Cancer advocates histopathologic assessment of tumor regression grade (TRG) but does not endorse a specific schema. We sought to examine the prognostic value of the recently revised National Comprehensive Cancer Network (NCCN) definition of TRG specifying TRG0 as no disease in primary tumor or lymph nodes.

PATIENTS AND METHODS

Patients with clinical-stage T2+/N+/M0 LRGC receiving preoperative chemotherapy and curative-intent gastrectomy were identified (2000-2020). TRG using the current NCCN definition was retrospectively assigned. Factors associated with TRG were examined using ordinal logistic regression and overall survival (OS) was assessed using the Kaplan-Meier method and Cox regression.

RESULTS

Among 117 patients, the most common chemotherapy regimen was epirubicin, cisplatin, plus fluorouracil or capecitabine (ECF/ECX) (n = 48, 41%), followed by folinic acid, fluorouracil, and oxaliplatin (FOLFOX) (n = 30, 26%), and fluorouracil, leucovorin, oxaliplatin, plus docetaxel (FLOT) (n = 13, 11%). TRG3 was the most common histopathologic response (n = 68, 58%), followed by TRG2 (n = 25, 21%), TRG1 (n = 18, 15%), and, lastly, TRG0 (n = 6, 5.1%). The only preoperative factor independently associated with lower TRG was gastroesophageal junction tumor location (OR 0.24, p = 0.012). Higher TRG was independently associated with worse OS in a stepwise fashion (HR 1.49, p = 0.026). Posttreatment pathologic lymph node status was the strongest prognostic factor (HR 1.93, p = 0.026). Independent prognostic value of TRG and ypT stage could not be shown due to substantial overlap.

CONCLUSIONS

TRG using the contemporary NCCN definition is associated with OS in LRGC. TRG0 is uncommon but with excellent prognosis. ypN status is the strongest prognostic factor and the revised NCCN definition acknowledging this is appropriate.