Effects of Sarcopenia, Myosteatosis, and the Prognostic Nutritional Index on Survival in Stage 2 and 3 Gastric Cancer Patients

Myosteatosis predicts postoperative complications and long-term survival in robotic gastrectomy for gastric cancer: A propensity score analysis

BACKGROUND

Robotic gastrectomy is increasingly utilized for gastric cancer, but high morbidity remains a concern. Myosteatosis or low skeletal muscle density reflecting fatty infiltration, associates with complications after other cancer surgeries but has not been evaluated for robotic gastrectomy.

METHODS

This retrospective study analysed 381 patients undergoing robotic gastrectomy for gastric cancer from September 2019 to October 2022. Myosteatosis was quantified on preoperative computed tomography (CT) images at lumbar 3 (L3). Propensity score matching addressed potential confounding between myosteatosis and non-myosteatosis groups. Outcomes were postoperative complications, 30 days mortality, 30 days readmissions and survival.

RESULTS

Myosteatosis was present in 33.6% of patients. Myosteatosis associated with increased overall (47.7% vs. 26.5%, p < 0.001) and severe complications (12.4% vs. 4.9%, p < 0.001). After matching, myosteatosis remained associated with increased overall complications, major complications, intensive care unit (ICU) transfer and readmission (all p < 0.05). Myosteatosis independently predicted overall [odds ratio (OR) = 2.86, 95% confidence interval (CI): 1.57-5.20, p = 0.001] and severe complications (OR = 4.81, 95% CI: 1.51-15.27, p = 0.008). Myosteatosis also associated with reduced overall (85.0% vs. 93.2%, p = 0.015) and disease-free survival (80.3% vs. 88.4%, p=0.029). On multivariate analysis, myosteatosis independently predicted poorer survival [hazard ratio (HR) = 2.83, 95% CI: 1.32-6.08, p=0.012] and disease-free survival (HR = 1.83, 95% CI: 1.01-3.30, p=0.032).

CONCLUSION

Preoperative CT-defined myosteatosis independently predicts increased postoperative complications and reduced long-term survival after robotic gastrectomy for gastric cancer. Assessing myosteatosis on staging CT could optimize preoperative risk stratification.

Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study

BACKGROUND

Although accurately evaluating the overall survival (OS) of gastric cancer patients remains difficult, radiomics is considered an important option for studying prognosis.

AIM

To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography (CT) image radiomics.

METHODS

This study included 181 stage II/III gastric cancer patients, 141 from Lichuan People's Hospital, and 40 from the Cancer Imaging Archive (TCIA). Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest (ROI), and approximately 1700 radiomics features were extracted from each ROI. The skeletal muscle index (SMI) and skeletal muscle density (SMD) were measured using CT images from the lower margin of the third lumbar vertebra. Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation, 36 radiomics features were identified as important predictors, and the OS-associated CT image radiomics score (OACRS) was calculated for each patient using these important predictors.

RESULTS

Patients with a high OACRS had a poorer prognosis than those with a low OACRS score (P < 0.05) and those in the TCIA cohort. Univariate and multivariate analyses revealed that OACRS was a risk factor [RR = 3.023 (1.896-4.365), P < 0.001] independent of SMI, SMD, and pathological features. Moreover, OACRS outperformed SMI and SMD and could improve OS prediction (P < 0.05).

CONCLUSION

A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.

Association between the Prognostic Nutritional Index and the Occurrence of Immune-Related Adverse Events

Immune-related adverse events (irAEs) affect all organs and are associated with various symptoms. The identification of biomarkers that can predict irAEs may be particularly clinically useful. This study aimed to investigate whether the prognostic nutritional index (PNI) before the initiation of immune checkpoint inhibitor (ICI) treatment can predict the occurrence of irAEs. We conducted a survey of 111 patients with cancer who were receiving ICI fixed-dose monotherapy at Saga University Hospital from the time each ICI became available until January 2020. We compared the PNI between the patients with and without irAE expression, established a cutoff value for PNI associated with the development of irAEs, and investigated the incidence of irAEs and progression-free survival (PFS) in groups divided by the cutoff value. Patients with irAEs had significantly higher PNI than did those without, and there was a significant association between PNI and irAEs after adjusting for potential factors (odds ratio, 1.12; 95% confidence interval, 1.03-1.21). In addition, PNI ≥44.2 was associated with a significantly higher incidence of irAEs (75.0% vs. 35.2%, p = 0.0001) and significantly longer PFS than PNI <44.2 (p = 0.025). In conclusion, pretreatment PNI may be associated with the risk of developing irAEs in patients with advanced recurrent solid tumors. When the PNI is ≥44.2, patient management is important for avoiding serious AEs because while the treatment may be effective, the occurrence of irAEs is a concern.

Prognostic impact of pretreatment skeletal muscle index and CONUT score in diffuse large B-cell Lymphoma

BACKGROUND

Although the prognostic value of the Controlling Nutritional Status (CONUT) score in diffuse large B-cell lymphoma (DLBCL) has been reported in several previous studies, its clinical relevance for the presence of sarcopenia has not been assessed.

METHODS

In this study, 305 DLBCL patients were reviewed. They were categorized into normal/mild (n = 219) and moderate/severe (n = 86) CONUT groups. Sarcopenia was assessed using the L3-skeletal muscle index measured by baseline computed tomography imaging. Based on CONUT score and sarcopenia, patients were grouped: A (normal/mild CONUT and no sarcopenia), B (either moderate/severe CONUT or sarcopenia, but not both), and C (both moderate/severe CONUT and sarcopenia).

RESULTS

The moderate/severe CONUT group showed higher rates of ≥ grade 3 febrile neutropenia, thrombocytopenia, non-hematologic toxicities, and early treatment discontinuation not related to disease progression, compared to the normal/mild CONUT group. The moderate/severe CONUT group had a lower complete response rate (58.1% vs. 80.8%) and shorter median overall survival (18.5 vs. 162.6 months) than the normal/mild group. Group C had the poorest prognosis with a median survival of 8.6 months, while groups A and B showed better outcomes (not reached and 60.1 months, respectively). Combining CONUT score and sarcopenia improved the predictive accuracy of the Cox regression model (C-index: 0.763), compared to the performance of using either CONUT score (C-index: 0.754) or sarcopenia alone (C-index: 0.755).

CONCLUSIONS

In conclusion, the moderate/severe CONUT group exhibited treatment intolerance, lower response, and poor prognosis. Additionally, combining CONUT score and sarcopenia enhanced predictive accuracy for survival outcomes compared to individual variables.

The possible relation between temporal muscle mass and glioblastoma multiforme prognosis via sarcopenia perspective

BACKGROUND

The optimal sarcopenia measurement method in patients with a diagnosis of glioblastoma multiforme (GBM) is unknown. It has been found that temporal muscle thickness (TMT) may reflect sarcopenia and be associated with survival, but the relationship between temporal muscle area (TMA) and GBM prognosis has never been evaluated before. The primary outcome of the study was to evaluate the relationship between TMA/TMT and overall survival (OS) time in newly diagnosed GBM patients.

METHODS

The data of patients who presented at the university hospital between January 2009 and January 2019 with a confirmed diagnosis of glioblastoma multiforme at the time of diagnosis were analyzed retrospectively. Temporal muscle thickness and TMA were measured retrospectively from preoperative MRIs of patients diagnosed with GBM. Due to the small number of patients and the failure to determine a cut-off value with acceptable sensitivity and specificity using ROC analysis, the median values were chosen as the cut-off value. The patients were basically divided into two according to their median TMT (6.6 mm) or TMA (452 mm2 ) values, and survival analysis was performed with the Kaplan-Meier analysis.

RESULTS

The median TMT value was 6.6 mm, and the median TMA value was 452 mm2 . The median overall survival (OS) was calculated as 25.8 months in patients with TMT < 6.6 mm, and 15.8 months in patients with TMT ≥ 6.6 mm (p = 0.29). The median overall survival (OS) of patients with TMA < 452mm2 was 26.3 months, and the group with TMA ≥ 452mm2 was 14.6 months (p = 0.06). The median disease-free survival was 18.3 months (%95 CI: 13.2-23.4) in patients with TMT < 6.6mm, while mDFS was 10.9 (%95 CI: 8.0-13.8) months in patients with TMT ≥ 6.6mm (p = 0.21). The median disease-free survival was found to be 21.0 months (%95 CI: 15.8-26.1) in patients with TMA < 452 mm2 and 10.5 months (%95 CI: 7.8-13.2) in patients with TMA ≥ 452 mm2 (p = 0.018).

DISCUSSION

No association could be demonstrated between TMT or TMA and OS of GBM patients. In addition, the median DFS was found to be longer in patients with low TMA. There is an unmet need to determine the optimal method of sarcopenia in GBM patients.