Recurrence in node-negative advanced gastric cancer: Novel findings from an in-depth pathological analysis of prognostic factors from a multicentric series

Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers

The relationship between 2-deoxy-2-[18F]fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) textural features and histopathological findings in gastric cancer has not been fully evaluated. We investigated the relationship between the textural features of primary tumors on FDG PET/CT with histopathological findings and recurrence-free survival (RFS) in patients with advanced gastric cancer (AGC). Fifty-six patients with AGC who underwent FDG PET/CT for staging work-ups were retrospectively enrolled. Conventional parameters and the first- and second-order textural features of AGC were extracted using PET textural analysis. Upon histopathological analysis, along with histopathological classification and staging, the degree of CD4, CD8, and CD163 cell infiltrations and expressions of interleukin-6 and matrix-metalloproteinase-11 (MMP-11) in the primary tumor were assessed. The histopathological classification, Lauren classification, lymph node metastasis, CD8 T lymphocyte and CD163 macrophage infiltrations, and MMP-11 expression were significantly associated with the textural features of AGC. The multivariate survival analysis showed that increased FDG uptake and intra-tumoral metabolic heterogeneity were significantly associated with an increased risk of recurrence after curative surgery. Textural features of AGC on FDG PET/CT showed significant correlations with the inflammatory response in the tumor microenvironment and histopathological features of AGC, and they showed significant prognostic values for predicting RFS.

Computed Tomography-Based Radiomics Nomogram: Potential to Predict Local Recurrence of Gastric Cancer After Radical Resection

Objective

Accurate prediction of postoperative recurrence risk of gastric cancer (GC) is critical for individualized precision therapy. We aimed to investigate whether a computed tomography (CT)-based radiomics nomogram can be used as a tool for predicting the local recurrence (LR) of GC after radical resection.

Materials and Methods

342 patients (194 in the training cohort, 78 in the internal validation cohort, and 70 in the external validation cohort) with pathologically proven GC from two centers were included. Radiomics features were extracted from the preoperative CT imaging. The clinical model, radiomics signature, and radiomics nomogram, which incorporated the radiomics signature and independent clinical risk factors, were developed and verified. Furthermore, the performance of these three models was assessed by using the area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

Results

The radiomics signature, which was comprised of two selected radiomics features, namely, contrast_GLCM and dissimilarity_GLCM, showed better performance than the clinical model in predicting the LR of GC, with AUC values of 0.83 in the training cohort, 0.84 in the internal validation cohort, and 0.73 in the external cohort, respectively. By integrating the independent clinical risk factors (N stage, bile acid duodenogastric reflux and nodular or irregular outer layer of the gastric wall) into the radiomics signature, the radiomics nomogram achieved the highest accuracy in predicting LR, with AUC values of 0.89, 0.89 and 0.80 in the three cohorts, respectively. DCA in the validation cohort showed that radiomics nomogram added more net benefit than the clinical model within the range of 0.01-0.98.

Conclusion

The CT-based radiomics nomogram has the potential to predict the LR of GC after radical resection.

RECURRENCE IN PN0 GASTRIC CANCER: RISK FACTORS IN THE OCCIDENT

BACKGROUND

Nearly 10% of node negative gastric cancer patients who underwent curative surgery have disease recurrence. Western data is extremely poor on this matter and identifying the risk factors that associate with relapse may allow new strategies to improve survival.

AIM

Verify the clinical and pathological characteristics that correlate with recurrence in node negative gastric cancer.

METHODS

All gastric cancer patients submitted to gastrectomy between 2009 and 2019 at our institution and pathologically classified as N0 were considered. Their data were available in a prospective database. Inclusion criteria were: gastric adenocarcinoma, node negative, gastrectomy with curative intent, R0 resection. Main outcomes studied were: disease-free survival and overall survival.

RESULTS

A total of 270 patients fulfilled the inclusion criteria. Mean age was 63-year-old and 155 were males. Subtotal gastrectomy and D2 lymphadenectomy were performed in 64% and 74.4%, respectively. Mean lymph node yield was 37.6. Early GC was present in 54.1% of the cases. Mean follow-up was 40.8 months and 19 (7%) patients relapsed. Disease-free survival and overall survival were 90.9% and 74.6%, respectively. Independent risk factors for worse disease-free survival were: total gastrectomy, lesion size ≥3.4 cm, higher pT status and <16 lymph nodes resected.

CONCLUSION

In western gastric cancer pN0 patients submitted to gastrectomy, lymph node count <16, pT3-4 status, tumor size ≥3.4 cm, total gastrectomy and presence of lymphatic invasion, are all risk factors for disease relapse.

Fluorescence-guided lymphadenectomy in gastric cancer: a prospective western series

BACKGROUND

Indocyanine green (ICG) has been recently introduced in clinical practice as a fluorescent tracer. Lymphadenectomy is particularly challenging in gastric cancer surgery, owing to the complex anatomical drainage.

AIM

The primary outcomes of this study were the feasibility and usefulness of ICG-guided lymphadenectomy in gastric cancer surgery, considering both the success rate and improved understanding of the surgical anatomy of nodal basins. The secondary outcome was the diagnostic ability of ICG to predict the presence of nodal metastases.

PATIENTS AND METHODS

We conducted a single-center prospective trial comprising 13 patients with gastric cancer. ICG was injected the afternoon prior to surgery or intraoperatively via the submucosal or subserosal route. Standard lymphadenectomy was performed in all patients, according to patient age and tumor stage, as usual, but after standard lymphadenectomy the residual ICG + nodes were harvested and analyzed. Each nodal station and each dissected node was recorded and classified as ICG + or ICG- (both in vivo and back table evaluation was utilized for classification). After pathological analysis, each nodal station and each dissected node was recorded as metastatic or nonmetastatic (E&E staining).

RESULTS

The feasibility rate was 84.6% (11/13). The mean number of dissected lymph nodes per patient was 37.9. Focusing on the 11 patients in whom ICG-guided nodal navigation was successfully performed, 81 lymph node stations were removed, for a total of 417 lymph nodes. Sixty-six stations (81.48%), comprising a total of 336 lymph nodes, exhibited fluorescence. No IC- node was metastatic; all 54 metastatic nodes were ICG + . A total of 282 ICG + nodes were nonmetastatic. In two cases, some nodes outside D2 areas were harvested, being ICG + (1 case of metastatic node).

CONCLUSIONS

Fluorescence lymphography-guided lymphadenectomy is a promising new technique that combines a high feasibility rate with considerable ease of use. Regarding its diagnostic value, the key finding from this prospective series is that no metastatic nodes were found outside fluorescent lymph node stations. Further studies are needed to investigate whether this technique can help surgeons performing standard lymphadenectomy and selecting cases for D2 + lymphadenectomy.

Nuclear shape, architecture and orientation features from H&E images are able to predict recurrence in node-negative gastric adenocarcinoma

BACKGROUND

Identifying intestinal node-negative gastric adenocarcinoma (INGA) patients with high risk of recurrence could help perceive benefit of adjuvant therapy for INGA patients following surgical resection. This study evaluated whether the computer-extracted image features of nuclear shapes, texture, orientation, and tumor architecture on digital images of hematoxylin and eosin stained tissue, could help to predict recurrence in INGA patients.

METHODS

A tissue microarrays cohort of 160 retrospectively INGA cases were digitally scanned, and randomly selected as training cohort (D1 = 60), validation cohort (D2 = 100 and D3 = 100, D2 and D3 are different tumor TMA spots from the same patient), accompanied with immunohistochemistry data cohort (D3' = 100, a duplicate cohort of D3) and negative controls data cohort (D5 = 100, normal adjacent tissues). After nuclear segmentation by watershed-based method, 189 local nuclear features were captured on each TMA core and the top 5 features were selected by Wilcoxon rank sum test within D1. A morphometric-based image classifier (NGAHIC) was composed across the discriminative features and predicted the recurrence in INGA on D2. The intra-tumor heterogeneity was assessed on D3. Manual nuclear atypia grading was conducted on D1 and D2 by two pathologists. The expression of HER2 and Ki67 were detected by immunohistochemistry on D3 and D3', respectively. The association between manual grading and INGA outcome was analysis.

RESULTS

Independent validation results showed the NGAHIC achieved an AUC of 0.76 for recurrence prediction. NGAHIC-positive patients had poorer overall survival (P = 0.017) by univariate survival analysis. Multivariate survival analysis, controlling for T-stage, histology stage, invasion depth, demonstrated NGAHIC-positive was a reproducible prognostic factor for poorer disease-specific survival (HR = 17.24, 95% CI 3.93-75.60, P < 0.001). In contrast, human grading was only prognostic for one reader on D2. Moreover, significant correlations were observed between NGAHIC-positive patients and positivity of HER2 and Ki67 labeling index.

CONCLUSIONS

The NGAHIC could provide precision oncology, personalized cancer management.