Preoperative Muscle-Adipose Index: A New Prognostic Factor for Gastric Cancer

Preoperative Prediction of Her-2 and Ki-67 Status in Gastric Cancer Using 18F-FDG PET/CT Radiomics Features of Visceral Adipose Tissue

Aims/Background Immunohistochemistry (IHC) is the main method to detect human epidermal growth factor receptor 2 (Her-2) and Ki-67 expression levels. However, IHC is invasive and cannot reflect their expression status in real-time. This study aimed to build radiomics models based on visceral adipose tissue (VAT)'s 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging, and to evaluate the relationship between radiomics features of VAT and positive expression of Her-2 and Ki-67 in gastric cancer (GC). Methods Ninety patients with GC were enrolled in this study. 18F-FDG PET/CT radiomics features were calculated using the PyRadiomics package. Two methods were employed to reduce radiomics features. The machine learning models, logistic regression (LR), and support vector machine (SVM), were constructed and estimated by the receiver operator characteristic (ROC) curve. The correlation of outstanding features with Ki-67 and Her-2 expression status was evaluated. Results For the Ki-67 set, the area under of the receiver operator characteristic curve (AUC) and accuracy were 0.86 and 0.79 for the LR model and 0.83 and 0.69 for the SVM model. For the Her-2 set, the AUC and accuracy were 0.84 and 0.86 for the LR model and 0.65 and 0.85 for the SVM model. The LR model for Ki-67 exhibited outstanding prediction performance. Three wavelet transform features were correlated with Her-2 expression status (p all < 0.001), and one wavelet transform feature was correlated with the expression status of Ki-67 (p = 0.042). Conclusion 18F-FDG PET/CT-based radiomics models of VAT demonstrate good performance in predicting Her-2 and Ki-67 expression status in patients with GC. Radiomics features can be used as imaging biomarkers for GC.

Enlightenment of robotic gastrectomy from 527 patients with gastric cancer in the minimally invasive era: 5 years of optimizing surgical performance in a high-volume center - a retrospective cohort study

BACKGROUND

Learning curves have been used in the field of robotic gastrectomy (RG). However, it should be noted that the previous study did not comprehensively investigate all changes related to the learning curve. This study aims to establish a learning curve for radical RG and evaluate its effect on the short-term outcomes of patients with gastric cancer.

METHODS

The clinicopathological data of 527 patients who underwent RG between August 2016 and June 2021 were retrospectively analyzed. Learning curves related to the operation time and postoperative hospital stay were determined separately using cumulative sum (CUSUM) analysis. Then, the impact of the learning curve on surgical efficacy was analyzed.

RESULTS

Combining the CUSUM curve break points and technical optimization time points, the entire cohort was divided into three phases (patients 1-100, 101-250, and 251-527). The postoperative complication rate and postoperative recovery time tended to decrease significantly with phase advancement ( P <0.05). More extraperigastric examined lymph nodes (LN) were retrieved in phase III than in phase I (I vs. III, 15.12±6.90 vs. 17.40±7.05, P =0.005). The rate of LN noncompliance decreased with phase advancement. Textbook outcome (TO) analysis showed that the learning phase was an independent factor in TO attainment ( P <0.05).

CONCLUSION

With learning phase advancement, the short-term outcomes were significantly improved. It is possible that our optimization of surgical procedures could have contributed to this improvement. The findings of this study facilitate the safe dissemination of RG in the minimally invasive era.

Association of preoperative muscle-adipose index measured by computed tomography with survival in patients with esophageal squamous cell carcinoma

BACKGROUND

Conventional nutritional metrics are closely associated with the prognosis of patients with radically resected esophageal squamous cell carcinoma (ESCC). Nevertheless, the prognostic implications of muscle and adipose tissue composite indexes in ESCC remain unknown.

METHODS

We retrospectively analyzed clinicopathological data of 304 patients who underwent resected ESCC. To obtain measurements of the muscle and adipose indexes, preoperative computed tomography (CT) images were used to quantify skeletal-muscle adipose tissue. The diagnostic threshold for muscle-adipose imbalance was determined using X-tile software and used to analyze the association between the muscle-adipose index (MAI) and survival. Instantaneous risk of recurrence was assessed using a hazard function. We constructed a nomogram based on the MAI and other clinical characteristics and established a novel predictive model with independent prognostic factors. The prognostic capabilities of these nomograms were evaluated using calibration curves, receiver operating characteristic (ROC) curves, and decision-curve analysis (DCA).

RESULTS

The overall survival (OS) and disease-free survival (DFS) rates in the muscle-adipose-balanced group were significantly better than those in the muscle-adipose-imbalanced group. Multivariate analyses revealed that the MAI, prognostic nutritional index (PNI), tumor stage, and tumor differentiation were independent prognostic factors for OS and DFS in patients with resected ESCC (P < 0.05). The nuclear density curve indicated a lower risk of recurrence for patients in the muscle-adipose-balanced group than that for their imbalanced counterparts. Conversely, the nuclear density curve for PNI was confounded. Postoperative radiotherapy- (RT) benefit analysis demonstrated that patients with ESCC in the muscle-adipose-balanced group could benefit from adjuvant RT.

CONCLUSION

This study confirmed that preoperative MAI could serve as a useful independent prognostic factor in patients with resected ESCC. A nomogram based on the MAI and other clinical characteristics could provide individualized survival prediction for patients receiving radical resection. Timely and appropriate nutritional supplements may improve treatment efficacy.

Clinical impact of myosteatosis measured by magnetic resonance imaging on long-term outcomes of hepatocellular carcinoma after radical hepatectomy

AIMS

A variety of factors have been reported to affect long-term outcomes after radical resection of hepatocellular carcinoma (HCC). However, the indicators remain controversial. The purpose of this study was to evaluate the relationship between myosteatosis of the multifidus muscle and long-term outcomes after radical surgery for HCC.

METHODS

We retrospectively analyzed clinicopathological data for 187 patients with HCC who underwent radical surgery at Tokushima University between January 2009 and December 2020 and measured the density of fat in the multifidus muscle at L3 on their preoperative magnetic resonance images (MRI). Associations of myosteatosis and clinicopathological factors with long-term outcomes were evaluated.

RESULTS

The patients were divided into a myosteatosis-negative group (n = 122) and a myosteatosis-positive group (n = 65). The cancer-specific survival rate after hepatectomy was significantly worse in the myosteatosis-positive group than in the myosteatosis-negative group (p = 0.03). Univariate analysis identified multiple tumors, stage III/IV disease, an alfa-fetoprotein level ≥ 10 ng/ml, PIVKA-II ≥ 400 AU/ml, vp(+) status, and myosteatosis to be prognostic factors for cancer-specific survival. Multivariate analysis revealed multiple tumors, an alfa-fetoprotein level ≥ 10 ng/ml, and myosteatosis to be independent prognostic factors.

CONCLUSIONS

Myosteatosis measured by MRI is a simple and useful predictor of the long-term outcome after radical surgery for HCC.