Concurrent lymphovascular and perineural invasion after preoperative therapy for gastric adenocarcinoma is associated with decreased survival

Development and External Validation of a CT-Based Radiomics Nomogram to Predict Perineural Invasion and Survival in Gastric Cancer: A Multi-institutional Study

RATIONALE AND OBJECTIVES

To develop and validate a radiomics nomogram utilizing CT data for predicting perineural invasion (PNI) and survival in gastric cancer (GC) patients.

MATERIALS AND METHODS

A retrospective analysis of 408 GC patients from two institutions: 288 patients from Institution I were divided 7:3 into a training set (n = 203) and a testing set (n = 85); 120 patients from Institution II served as an external validation set. Radiomics features were extracted and screened from CT images. Independent radiomics, clinical, and combined models were constructed to predict PNI. Model discrimination, calibration, clinical utility, and prognostic significance were evaluated using area under the curve (AUC), calibration curves, decision curves analysis, and Kaplan-Meier curves, respectively.

RESULTS

15 radiomics features and three clinical factors were included in the final analysis. The AUCs of the radiomics model in the training, testing, and external validation sets were 0.843 (95% CI: 0.788-0.897), 0.831 (95% CI: 0.741-0.920), and 0.802 (95% CI: 0.722-0.882), respectively. A nomogram was developed by integrating significant clinical factors with radiomics features. The AUCs of the nomogram in the training, testing, and external validation sets were 0.872 (95% CI: 0.823-0.921), 0.862 (95% CI: 0.780-0.944), and 0.837 (95% CI: 0.767-0.908), respectively. Survival analysis revealed that the nomogram could effectively stratify patients for recurrence-free survival (Hazard Ratio: 4.329; 95% CI: 3.159-5.934; P < 0.001).

CONCLUSION

The radiomics-derived nomogram presented a promising tool for predicting PNI in GC and held significant prognostic implications.

IMPORTANT FINDINGS

The nomogram functioned as a non-invasive biomarker for determining the PNI status. The predictive performance of the nomogram surpassed that of the clinical model (P < 0.05). Furthermore, patients in the high-risk group stratified by the nomogram had a significantly shorter RFS (P < 0.05).

The relationship between the Prognostic Nutritional Index and lymphovascular and perineural invasion of the tumor in patients diagnosed with gastric cancer, and its effect on overall survival

A low Prognostic Nutritional Index (PNI) value, lymphovascular invasion (LVI), and perineural invasion (PeNI) have been identified as indicators of poor prognosis for many malignancies. We aimed to evaluate the relationship between PNI and LVI/PeNI, their prognostic significance, and their effect on overall survival in gastric cancer patients who underwent curative gastrectomy. A cutoff value of 39.8 was taken for the PNI, and PNI < 39.8 was defined as moderate to severe malnutrition. Patients were grouped as PNI-low (PNI < 39.8) and PNI-high (PNI ≥ 39.8). Paraffin-embedded tissue sections of surgical specimens were used to evaluate PeNI as defined by previously reported criteria. The study included 270 patients with ages ranging from 23 to 90 years. The mean PNI was calculated as 39.8 ± 6.35. PeNI was detected in 232 patients (85.93%), and LVI was identified in 248 patients (91.85%). It was observed that the PNI value of patients with an expired status in the PNI < 39.8 group was lower compared to those who survived, and in patients with PNI > 39.8, those without PeNI had better survival. The presence of PeNI in patients with PNI > 39.8 increased the mortality risk by 2.088 units, while in patients with PNI > 39.8, it was found that those without LVI had better survival, and the presence of LVI increased the mortality risk by 3.171 units. Mortality developed in 166 patients (61.48%) during the five-year follow-up period. The five-year overall survival was found to be 31.02 ± 21.73 months. In patients with gastric cancer, the PNI, LVI, and PeNI are independent prognostic factors for overall survival in postoperative patients. A low PNI score is an inherently poor prognostic factor. In patients with a high PNI score, the presence of positive LVI and PeNI negatively impacts survival. We found that in patients with a low PNI, the rates of PeNI and LVI are higher compared to those with a high PNI, and this significantly affects mortality.

Posterior and Para-Aortic (D2plus) Lymphadenectomy after Neoadjuvant/Conversion Therapy for Locally Advanced/Oligometastatic Gastric Cancer

Super-extended (D2plus) lymphadenectomy after chemotherapy has been reported in only a few studies. This retrospective study evaluates survival outcomes in a Western cohort of locally advanced or oligometastatic gastric cancer patients who underwent D2plus lymphadenectomy after neoadjuvant chemotherapy. A total of 97 patients treated between 2010 and 2022 were included. Of these, 62 had clinical stage II/III disease, and 35 had stage IV disease. Most patients (65%) received preoperative DOC/FLOT chemotherapy. The mean number of lymph nodes harvested was 39. Pathological positive nodes in the posterior/para-aortic stations occurred in 17 (17.5%) patients. Lymphovascular invasion, ypN stage, clinical stage, and perineural invasion were predictive factors for positive posterior/para-aortic nodes. Postoperative complications occurred in 21 patients, whereas severe complications (grade III or more) occurred in 9 cases (9.3%). Mortality rate was 1%. Median overall survival (OS) was 59 months (95% CI: 13-106), with a five-year survival rate of 49 ± 6%; the five-year OS after R0 surgery was 60 ± 7%. In patients with positive posterior/para-aortic nodes, the median OS was 15 months (95% CI: 13-18). D2plus lymphadenectomy after chemotherapy for locally advanced or oligometastatic gastric cancer is feasible and associated with low morbidity/mortality rates. The incidence of pathological metastases in posterior/para-aortic nodes is not negligible even after systemic chemotherapy, with poor long-term survival.

Dysregulation of core neurodevelopmental pathways-a common feature of cancers with perineural invasion

Background: High nerve density in tumors and metastasis via nerves (perineural invasion-PNI) have been reported extensively in solid tumors throughout the body including pancreatic, head and neck, gastric, prostate, breast, and colorectal cancers. Ablation of tumor nerves results in improved disease outcomes, suggesting that blocking nerve-tumor communication could be a novel treatment strategy. However, the molecular mechanisms underlying this remain poorly understood. Thus, the aim here was to identify molecular pathways underlying nerve-tumor crosstalk and to determine common molecular features between PNI-associated cancers. Results: Analysis of head and neck (HNSCC), pancreatic, and gastric (STAD) cancer Gene Expression Omnibus datasets was used to identify differentially expressed genes (DEGs). This revealed extracellular matrix components as highly dysregulated. To enrich for pathways associated with PNI, genes previously correlated with PNI in STAD and in 2 HNSCC studies where tumor samples were segregated by PNI status were analyzed. Neurodevelopmental genes were found to be enriched with PNI. In datasets where tumor samples were not segregated by PNI, neurodevelopmental pathways accounted for 12%-16% of the DEGs. Further dysregulation of axon guidance genes was common to all cancers analyzed. By examining paralog genes, a clear pattern emerged where at least one family member from several axon guidance pathways was affected in all cancers examined. Overall 17 different axon guidance gene families were disrupted, including the ephrin-Eph, semaphorin-neuropilin/plexin, and slit-robo pathways. These findings were validated using The Cancer Genome Atlas and cross-referenced to other cancers with a high incidence of PNI including colon, cholangiocarcinoma, prostate, and breast cancers. Survival analysis revealed that the expression levels of neurodevelopmental gene families impacted disease survival. Conclusion: These data highlight the importance of the tumor as a source of signals for neural tropism and neural plasticity as a common feature of cancer. The analysis supports the hypothesis that dysregulation of neurodevelopmental programs is a common feature associated with PNI. Furthermore, the data suggested that different cancers may have evolved to employ alternative genetic strategies to disrupt the same pathways. Overall, these findings provide potential druggable targets for novel therapies of cancer management and provide multi-cancer molecular biomarkers.

The value of machine learning based radiomics model in preoperative detection of perineural invasion in gastric cancer: a two-center study

Purpose

To establish and validate a machine learning based radiomics model for detection of perineural invasion (PNI) in gastric cancer (GC).

Methods

This retrospective study included a total of 955 patients with GC selected from two centers; they were separated into training (n=603), internal testing (n=259), and external testing (n=93) sets. Radiomic features were derived from three phases of contrast-enhanced computed tomography (CECT) scan images. Seven machine learning (ML) algorithms including least absolute shrinkage and selection operator (LASSO), naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. A combined model was constructed by aggregating the radiomic signatures and important clinicopathological characteristics. The predictive ability of the radiomic model was then assessed with receiver operating characteristic (ROC) and calibration curve analyses in all three sets.

Results

The PNI rates for the training, internal testing, and external testing sets were 22.1, 22.8, and 36.6%, respectively. LASSO algorithm was selected for signature establishment. The radiomics signature, consisting of 8 robust features, revealed good discrimination accuracy for the PNI in all three sets (training set: AUC = 0.86; internal testing set: AUC = 0.82; external testing set: AUC = 0.78). The risk of PNI was significantly associated with higher radiomics scores. A combined model that integrated radiomics and T stage demonstrated enhanced accuracy and excellent calibration in all three sets (training set: AUC = 0.89; internal testing set: AUC = 0.84; external testing set: AUC = 0.82).

Conclusion

The suggested radiomics model exhibited satisfactory prediction performance for the PNI in GC.

Clinical significance of the largest histopathological metastatic lymph node size for postoperative course of patients undergoing surgery for gastric cancer

Aim

The aim of this study was to investigate the effect of the largest metastatic lymph node (MLN) size on postoperative outcomes of patients with stage II-III gastric cancer (GC).

Methods

A total of 163 patients with stage II/III GC who underwent curative surgery were included in this single-center retrospective study. The lymph nodes were counted, each lymph node was analyzed for metastatic involvement by histopathological examination, and the diameter of the largest metastatic lymph node was recorded. The severity of postoperative complications was assessed by Clavien-Dindo classification system. Two groups of 163 patients were defined according to ROC analysis with cut-off value of histopathologically maximum MLN diameter. A comparative analysis of demographic and clinicopathological characteristics of the patients and their postoperative outcomes were performed.

Results

The median hospital stay was significantly longer in patients with major complications compared to patients without major complications [18 days (IQR: 13-24) vs. 8 days (IQR: 7-11); (p < 0.001)]. The median MLN size was significantly larger in deceased patients compared to survived [1.3 cm (IQR: 0.8-1.6) vs. 0.9 cm (IQR: 0.6-1.2), respectively; (p < 0.001)]. The cut-off value of MLN size predicting mortality was found as 1.05 cm. MLN size ≥1.05 cm had nearly 3.5 times more negative impact on survival.

Conclusions

The largest metastatic lymph node size had a significant association with survival outcomes. Particularly, MLN size over 1.05 cm was associated with worse survival outcomes. However, the largest MLN was not shown to have any effect on major complications. Further prospective and large-scale studies are required to draw more precise conclusions.

Lymph node ratio is a prospective prognostic indicator for locally advanced gastric cancer patients after neoadjuvant chemotherapy

Background

The accuracy of lymph node ratio (LNR) as a prognostic index remains to be proven for gastric cancer patients after neoadjuvant chemotherapy (NACT). This study sought to investigate the prognostic value of LNR in locally advanced gastric cancer (LAGC) patients after NACT.

Methods

LAGC patients with clinical TNM stages 2–3, Her2(−), and Eastern Cooperative Oncology Group, scores 0–2 are routinely scheduled with NACT. Patients with LAGC after NACT and surgical operation between January 2012 and October 2020 were retrospectively reviewed. The correlation between LNR and survival was investigated.

Results

Overall, 148 patients were enrolled: 103 with low-LNR (LNR ≤ 30%) and 45 with high-LNR (LNR > 30%). Approximately, 50.5% and 24.4% patients responded to NACT at the primary site in the low-LNR and high-LNR groups, respectively. The overall survival (OS) and progression-free survival (PFS) of low-LNR group were considerably better than those of high-LNR group (3-year OS: 81.9% vs 18.5%, P < 0.001; 3-year PFS: 72.6% vs 13.5%, P < 0.001). In the low-LNR group, OS and PFS were superior in patients with tumor regression grade (TRG) 0–2 than in those with TRG 3 (3-year OS: 89.2% vs 73.2%, P = 0.086; 3-year PFS: 80.3% vs 66.5%, P = 0.036). In association with OS and PFS, the degree of tumor differentiation, TRG, and LNR were identified as predictive factors, and LNR was identified as the independent prognostic factor in univariate and multivariate analyses, respectively.

Conclusions

LNR is a prospective index of prognosis in patients with LAGC after NACT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02725-9.

Quantitative Dynamic-Enhanced MRI and Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Prediction of the Pathological Response to Neoadjuvant Chemotherapy and the Prognosis in Locally Advanced Gastric Cancer

Background

This study aimed to explore the predictive value of quantitative dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) quantitative parameters for the response to neoadjuvant chemotherapy (NCT) in locally advanced gastric cancer (LAGC) patients, and the relationship between the prediction results and patients’ prognosis, so as to provide a basis for clinical individualized precision treatment.

Methods

One hundred twenty-nine newly diagnosed LAGC patients who underwent IVIM-DWI and DCE-MRI pretreatment were enrolled in this study. Pathological tumor regression grade (TRG) served as the reference standard of NCT response evaluation. The differences in DCE-MRI and IVIM-DWI parameters between pathological responders (pR) and pathological non-responders (pNR) groups were analyzed. Univariate and multivariate logistic regressions were used to identify independent predictive parameters for NCT response. Prediction models were built with statistically significant quantitative parameters and their combinations. The performance of these quantitative parameters and models was evaluated using receiver operating characteristic (ROC) analysis. Clinicopathological variables, DCE-MRI and IVIM-DWI derived parameters, as well as the prediction model were analyzed in relation to 2-year recurrence-free survival (RFS) by using Cox proportional hazards model. RFS was compared using the Kaplan–Meier method and the log-rank test.

Results

Sixty-nine patients were classified as pR and 60 were pNR. K^trans, k_ep, and v_e values in the pR group were significantly higher, while ADC_standard and D values were significantly lower than those in the pNR group. Multivariate logistic regression analysis demonstrated that K^trans, k_ep, v_e, and D values were independent predictors for NCT response. The combined predictive model, which consisted of DCE-MRI and IVIM-DWI, showed the best prediction performance with an area under the curve (AUC) of 0.922. Multivariate Cox regression analysis showed that ypStage III and NCT response predicted by the IVIM-DWI model were independent predictors of poor RFS. The IVIM-DWI model could significantly stratify median RFS (52 vs. 15 months) and 2-year RFS rate (72.3% vs. 21.8%) of LAGC.

Conclusion

Pretreatment DCE-MRI quantitative parameters K^trans, k_ep, v_e, and IVIM-DWI parameter D value were independent predictors of NCT response for LAGC patients. The regression model based on baseline DCE-MRI, IVIM-DWI, and their combination could help RFS stratification of LAGC patients.