Role of CT in the prediction of pathological complete response in gastric cancer after neoadjuvant chemotherapy

Transcriptomics-Based Liquid Biopsy for Early Detection of Recurrence in Locally Advanced Gastric Cancer

The study presents a transcriptomics-based liquid biopsy approach for early recurrence detection in locally advanced gastric cancer (LAGC). Four mRNA biomarkers (AGTR1, DNER, EPHA7, and SUSD5) linked to recurrence are identified through transcriptomic data analysis. A Risk Stratification Assessment (RSA) model combining these biomarkers with clinical features showed superior predictive accuracy for postoperative recurrence, with AUCs of 0.919 and 0.935 in surgical and liquid biopsy validation cohorts, respectively. Functional studies using human gastric cancer cell lines AGS and HGC-27 demonstrated that silencing the identified mRNA panel genes impaired cell migration, invasion, and proliferation. In vivo experiments further showed reduced tumor growth, metastasis, and lymphangiogenesis in mice, possibly mediated by the cAMP signaling pathway. This non-invasive approach offers significant potential for enhancing recurrence detection and enabling personalized treatment strategies, thereby improving patient outcomes in the management of LAGC.

Advanced Gastric Cancer: CT Radiomics Prediction of Lymph Modes Metastasis After Neoadjuvant Chemotherapy

This study aims to create and assess machine learning models for predicting lymph node metastases following neoadjuvant treatment in advanced gastric cancer (AGC) using baseline and restaging computed tomography (CT). We evaluated CT images and pathological data from 158 patients with resected stomach cancer from two institutions in this retrospective analysis. Patients were eligible for inclusion if they had histologically proven gastric cancer. They had received neoadjuvant chemotherapy, with at least 15 lymph nodes removed. All patients received baseline and preoperative abdominal CT and had complete clinicopathological reports. They were divided into two cohorts: (a) the primary cohort (n = 125) for model creation and (b) the testing cohort (n = 33) for evaluating models' capacity to predict the existence of lymph node metastases. The diagnostic ability of the radiomics-model for lymph node metastasis was compared to traditional CT morphological diagnosis by radiologist. The radiomics model based on the baseline and preoperative CT images produced encouraging results in the training group (AUC 0.846) and testing cohort (AUC 0.843). In the training cohort, the sensitivity and specificity were 81.3% and 77.8%, respectively, whereas in the testing cohort, they were 84% and 75%. The diagnostic sensitivity and specificity of the radiologist were 70% and 42.2% (using baseline CT) and 46.3% and 62.2% (using preoperative CT). In particular, the specificity of radiomics model was higher than that of conventional CT in diagnosing N0 cases (no lymph node metastasis). The CT-based radiomics model could assess lymph node metastasis more accurately than traditional CT imaging in AGC patients following neoadjuvant chemotherapy.

Spectral CT vs. diffusion-weighted imaging for the quantitative prediction of pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer

OBJECTIVES

To compare the performance of spectral CT and diffusion-weighted imaging (DWI) for predicting pathologic response after neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer (LAGC).

MATERIALS AND METHODS

This was a retrospective analysis drawn from a prospective dataset. Sixty-five patients who underwent baseline concurrent triple-phase enhanced spectral CT and DWI-MRI and standard NAC plus radical gastrectomy were enrolled, and those with poor images were excluded. The tumor regression grade (TRG) was the reference standard, and patients were classified as responders (TRG 0 + 1) or non-responders (TRG 2 + 3). Quantitative iodine concentration (IC), normalized IC (nIC), and apparent diffusion coefficient (ADC) were measured by placing a freehand region of interest manually on the maximal two-dimensional plane. Their differences between responders and non-responders were compared. The performances of significant parameters were evaluated by the receiver operating characteristic analysis. The correlations between parameters and TRG status were explored through Spearman correlation coefficient test. Kaplan-Meier survival analysis was adopted to analyze their relationship with patient survival.

RESULTS

nICDP and ADC were associated with the TRG and yielded comparable performances for predicting TRG categories, with area under the curve (AUC) of 0.674 and 0.673, respectively. Their combination achieved a significantly increased AUC of 0.770 (p ; 0.05) and was associated with patient disease-free survival, with hazard ratio of 2.508 (1.043-6.029).

CONCLUSION

Spectral CT and DWI were equally useful imaging techniques for predicting pathologic response to NAC in LAGC. The combination of nICDP and ADC gained significant incremental benefits and was related to patient disease-free survival.

CLINICAL RELEVANCE STATEMENT

Spectral CT and DWI-based quantitative measurements are effective markers for predicting the pathologic regression outcomes of locally advanced gastric cancer patients after neoadjuvant chemotherapy.

KEY POINTS

• The pathologic tumor regression grade, the standard criteria for treatment response after neoadjuvant chemotherapy in gastric cancer patients, is difficult to predict early. • The quantitative parameters of normalized iodine concentration at delay phase and apparent diffusion coefficients were correlated with pathologic response; their combination demonstrated incremental benefits and was associated with patient disease-free survival. • Spectral CT and DWI are equally useful imaging modalities for predicting tumor regression grade after neoadjuvant chemotherapy in patients with locally advanced gastric cancer.

Predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer: The establishment of a spectral CT-based nomogram from prospective datasets

BACKGROUND

To establish a spectral CT-based nomogram for predicting early neoadjuvant chemotherapy (NAC) response for locally advanced gastric cancer (LAGC).

METHODS

This study prospectively recruited 222 cases (177 male and 45 female patients, 9.59 ± 9.54 years) receiving NAC and radical gastrectomy. Triple enhanced spectral CT scans were performed before NAC initiation. According to post-operative tumor regression grade (TRG), patients were classified into responders (TRG = 0 + 1) or non-responders (TRG = 2 + 3), and split into a primary (156) and validation (66) dataset at 7:3 ratio chronologically. We compared clinicopathological data, follow-up information, iodine concentration (IC), normalized ICs (nICs) in arterial/venous/delayed phases (AP/VP/DP) between responders and non-responders. Independent risk factors of response were screened by multivariable logistic regression and adopted for model construction. Model was visualized by nomograms and its capability was determined through receiver operating characteristic (ROC) curves. Log-rank survival analysis was conducted to explore associations between TRG, nomogram and patients' survival.

RESULTS

This work identified Borrmann classification, ICDP, and nICDP were independent risk factors of response outcomes. A spectral CT-based nomogram was built accordingly and achieved an area under the curve (AUC) of 0.797 (0.692-0.879) and 0.741(0.661-0.811) for the primary and validation dataset, respectively, higher than AUC of individual parameters alone. The nomogram was related to disease-free survival in the validation dataset (Hazard ratio (HR): 5.19 [1.18-12.93], P = 0.02).

CONCLUSIONS

The spectral CT-based nomogram provides an efficient tool for predicting the pathologic response outcomes of GC after NAC and disease-free survival risk stratification.

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.

Imaging advances in efficacy assessment of gastric cancer neoadjuvant chemotherapy

Effective neoadjuvant chemotherapy (NAC) can improve the survival of patients with locally progressive gastric cancer, but chemotherapeutics do not always exhibit good efficacy in all patients. Therefore, accurate preoperative evaluation of the effect of neoadjuvant therapy and the appropriate selection of surgery time to minimize toxicity and complications while prolonging patient survival are key issues that need to be addressed. This paper reviews the role of three imaging methods, morphological, functional, radiomics, and artificial intelligence (AI)-based imaging, in evaluating NAC pathological reactions for gastric cancer. In addition, the advantages and disadvantages of each method and the future application prospects are discussed.

Clinical outcomes of conversion surgery following immune checkpoint inhibitors and chemotherapy in stage IV gastric cancer

BACKGROUND

The clinical benefit of conversion surgery following immunochemotherapy in patients with stage IV gastric cancer (GC) remains uncertain. This study aims to clarify the clinical outcomes of conversion surgery for such patients.

METHODS

This retrospective cohort study enroled consecutive patients with stage IV GC treated with a combination of immune checkpoint inhibitors and chemotherapy and/or anti-human epidermal growth factor receptor-2 targeted therapy as first-line therapy. Cumulative survival curves were estimated using Kaplan-Meier method. Logistic regression and Cox regression analyses were conducted to identify factors associated with conversion surgery and survival, respectively.

RESULTS

Among the 136 patients included in the study. The disease control rate was 72.1% (98/136), with objective response rate in 58.8% (80/136) and complete response rate in 5.9% (8/136). Among 98 patients with disease control, 56 patients underwent palliative immunochemotherapy with median progression-free survival (PFS) and overall survival at 9.2 and 16.2 months, respectively; the remaining 42 patients underwent conversion surgery, yielding an unreached median PFS over a 19.0-month median follow-up, accompanied by 1-year overall survival and PFS rates of 96.6% and 89.1%, respectively. The R0 resection rate reached 90.5% (38/42). 7 out of 42 patients achieved pathological complete response, of whom three patients demonstrated human epidermal growth factor receptor-2 positivity. No serious complications leading to death were observed during the perioperative period. Multivariate analysis indicated that programmed death ligand 1 combined positive score greater than or equal to 5 (odds ratio, 0.22; 95% CI, 0.08-0.57; P =0.002) favored successful conversion surgery, while signet ring cell carcinoma (hazard ratio, 6.29; 95% CI, 1.56-25.36; P =0.010) was the poor prognostic factor associated with survival in patients who underwent conversion surgery.

CONCLUSIONS

Conversion surgery holds the potential for significant survival benefits in stage IV GC patients who have achieved a favourable clinical response to immunochemotherapy. Individuals with signet ring cell carcinoma may experience increased post-conversion surgery recurrence.

The largest lymph node defined response to neoadjuvant chemotherapy can predict long-term prognosis in locally advanced gastric cancer

BACKGROUND

The assessment of tumor response to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC) remain challenging. We aimed to explore the potential role of peri-NACT change of the largest lymph node (LN) and primary tumor (P-T) in the prediction of tumor response and patient overall survival (OS) in LAGC.

METHODS

A cohort of LAGC patients who underwent NACT followed by radical surgery from a prospective clinical trial were retrospectively analyzed. The percentage change of the largest LN and P-T from initial to post-NACT Computed Tomography (CT) were measured. Tumor response was defined by the change of LN (LN-response) and P-T (P-T-response), respectively. A multivariate Cox model was constructed to examine if P-T- and LN-determined response had significant predictive ability for OS when adjusting with other possible prognostic factors.

RESULTS

Of the 41 patients, 28 (68.3%) was defined as LN-responders to NACT, and 17 (41.5%) patients was defined as P-T-responders. When the cohort was stratified by LN response standard, LN-responders showed a significant longer median OS than LN-nonresponders (p = 0.031, 20.6 vs 16.6 months). When stratified by primary tumor response, no significant difference in OS was observed between P-T-responders and P-T-nonresponders (p = 0.377, 18.5 vs 19.0 months). In the multivariate analysis, number of positive LN (p = 0.004, hazard ratio [HR] = 1.284), recurrence (p = 0.024, HR =3556), LN shrinkage (p = 0.022, HR = 0.930) and LN-response (p = 0.033, HR = 0.008) were observed with independent association with OS.

CONCLUSIONS

Peri-NACT change of the largest LN could reflect tumor response to NACT, and LN-defined response was useful in predicting the long-term prognosis (OS) of LAGC patients who underwent NACT followed by radical surgery.

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.

Current and future imaging modalities in gastric cancer

Gastric adenocarcinoma treatment can include endoscopic mucosal resection, surgery, chemotherapy, radiation, and palliative measures depending on staging. Both invasive and noninvasive staging techniques have been used to dictate the best treatment pathway. Here, we review the current imaging modalities used in gastric cancer as well as novel techniques to accurately stage and screen these patients.

AutoBRB: An automated belief rule base model for pathologic complete response prediction in gastric cancer

Gastric cancer is one of the most severe malignant lesions. Neoadjuvant chemotherapy (NAC) has proven to be an effective method in gastric cancer treatment, and patients who achieved the pathologic complete response (pCR) after NAC can improve survival time further. To accurately predict pCR in an interpretable way, a new automated belief rule base (AutoBRB) model is developed with careful data analysis in this paper. In AutoBRB, to determine the referential values that are important for the rule building, both the information gain ratio and expert knowledge are used, while a table-based strategy is designed to initialize the belief degrees for each rule. Then, the differential evolution (DE) algorithm is employed and modified for model optimization to improve the model's performance. Finally, with the help of training data, an adaptive searching strategy is designed to set the confidence threshold for the final prediction. The experimental results demonstrate that AutoBRB shows a more reasonable performance on the prediction of pCR.

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

Objective

To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC).

Methods

A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed.

Result

Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model.

Conclusion

The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.