Extranodal soft tissue metastasis as an independent prognostic factor in gastric cancer patients aged under 70 years after curative gastrectomy

Tumor deposits in gastric cancer cannot be regarded as metastatic lymph nodes: A single-center retrospective study

BACKGROUND

In gastric cancer (GC), the significance of the number of tumor deposits (TDs) in prognostic evaluation remains a subject of debate. This study aims to investigate whether TDs can be equated to regional metastatic lymph nodes, potentially improving the accuracy of prognostic assessments in patients with TDs.

METHODS

A retrospective analysis of clinicopathologic and follow-up data from patients who underwent radical gastrectomy at Yijishan Hospital of Wannan Medical College over a decade, from January 2012 to December 2021, was conducted. Patients were classified into TDs-negative and TDs-positive groups on the basis of the detection of TDs in their postoperative pathology reports.

RESULTS

The study included 4972 patients, with 575 (11.56 %) identified as having TDs. Among these, 524 TDs-positive patients were matched at a 1:1 ratio with 524 TDs-negative patients. Under the original TNM staging system, the chi-square (χ2) value was 58.234, with a C-index of 0.593. When TDs were classified as regional metastatic lymph nodes, the χ2 value for the modified TNM staging system rose to 72.269, with an improved C-index of 0.609. Nevertheless, the prognosis within the TDs-positive subgroups IIIa, IIIb, and IIIc was still significantly worse than those in the TDs-negative subgroup, even when TDs were reclassified for staging purposes (P < 0.001).

CONCLUSION

Although treating TDs as regional metastatic lymph nodes can increase the accuracy of disease staging in GC patients, it does not necessarily convey the true prognostic value of TDs.

Construction and validation of a prognostic model for gastric cancer patients with tumor deposits

Background

Tumor deposits (TD) was a significant risk factor impacting the prognosis of patients diagnosed with gastric cancer (GC), yet it was not currently incorporated into TNM staging systems. The objective of this research was to develop a predictive model for assessing the prognosis of patients with TD-positive GC.

Methods

Retrospective analysis was performed on the data of 4,972 patients treated for GC with D2 radical gastrectomy at Wannan Medical College's Yijishan Hospital between January 2012 and December 2021. The patients were categorized based on the number of TD (L1: 1, L2: 2-3, L3: ≥4) and the anatomical location of TD (Q1: single area, Q2: multiple areas). In a 3:1 ratio, patients were randomly assigned to one of two groups: training or validation.

Results

The study included a total of 575 patients who were divided into the training group (n = 432) and validation group (n = 143). Survival analysis showed that the number and anatomical location of TD had a significant impact on the prognosis of patients with TD-positive GC. Univariate analysis of the training group data revealed that tumor size, T-stage, N-stage, histological grade, number and distribution of TD, neural invasion, and postoperative chemotherapy were associated with prognosis. Multivariate Cox regression analysis identified poor histological grade, T4 stage, N3 stage, number of TD, neural invasion, and postoperative chemotherapy as independent prognostic factors for GC patients with TD. A nomogram was developed using these variables, demonstrating well predictive ability for 1, 3, and 5-year overall survival (OS) in the validation set. The DCA curve shows that the constructed model shows a large positive net gain compared to the eighth edition Tumour, Node, Metastasis (TNM) staging system.

Conclusion

The prognostic model developed for patients with TD-positive GC has a higher clinical utility compared to the eighth edition of TNM staging.

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer

BACKGROUND

The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet.

PURPOSE

This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value.

METHODS

A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS).

RESULTS

The combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650-0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis.

CONCLUSIONS

The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.

Clinical features and survival of patients with multiple primary malignancies

BACKGROUND

Multiple primary malignancies (MPM) are characterized by two or more primary malignancies in the same patient, excluding relapse or metastasis of prior cancer. We aimed to elucidate the clinical features and survival of MPM patients.

AIM

To elucidate the clinical features and survival of MPM patients.

METHODS

A retrospective study of MPM patients was conducted in our hospital between June 2016 and June 2019. Overall survival (OS) was calculated using the Kaplan-Meier method. The log-rank test was used to compare the survival of different groups.

RESULTS

A total of 243 MPM patients were enrolled, including 222 patients with two malignancies and 21 patients with three malignancies. Of patients with two malignancies, 51 (23.0%) had synchronous MPM, and 171 (77.7%) had metachronous MPM. The most common first cancers were breast cancer (33, 14.9%) and colorectal cancer (31, 14.0%). The most common second cancers were non-small cell lung cancer (NSCLC) (66, 29.7%) and gastric cancer (24, 10.8%). There was no survival difference between synchronous and metachronous MPM patients (36.4 vs 35.3 mo, P = 0.809). Patients aged > 65 years at diagnosis of the second cancer had a shorter survival than patients ≤ 65 years (28.4 vs 36.4 mo, P = 0.038). Patients with distant metastasis had worse survival than patients without metastasis (20.4 vs 86.9 mo, P = 0.000). Following multivariate analyses, age > 65 years and distant metastasis were independent adverse prognostic factors for OS.

CONCLUSION

During follow-up of a first cancer, the occurrence of a second or more cancers should receive greater attention, especially for common concomitant MPM, to ensure early detection and treatment of the subsequent cancer.