CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma

Body composition radiomic features as a predictor of survival in patients with non-small cellular lung carcinoma: A multicenter retrospective study

OBJECTIVES

This study combined two novel approaches in oncology patient outcome predictions-body composition and radiomic features analysis. The aim of this study was to validate whether automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.

METHODS

The study included 178 patients with non-small cell lung cancer receiving concurrent platinum-based chemoradiotherapy. Abdominal imaging was conducted as a part of whole-body positron emission tomography/computed tomography performed before therapy. Methods used included automated assessment of the volume of interest using densely connected convolutional network classification model - DenseNet121, automated muscle and adipose tissue segmentation using U-net architecture implemented in nnUnet framework, and radiomic features extraction. Acquired body composition radiomic features and clinical data were used for overall and 1-y survival prediction using machine learning classification algorithms.

RESULTS

The volume of interest detection model achieved the following metric scores: 0.98 accuracy, 0.89 precision, 0.96 recall, and 0.92 F1 score. Automated segmentation achieved a median dice coefficient >0.99 in all segmented regions. We extracted 330 body composition radiomic features for every patient. For overall survival prediction using clinical and radiomic data, the best-performing feature selection and prediction method achieved areas under the curve-receiver operating characteristic (AUC-ROC) of 0.73 (P < 0.05); for 1-y survival prediction AUC-ROC was 0.74 (P < 0.05).

CONCLUSION

Automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.

Impact of Altered Body Composition on Clinical and Oncological Outcomes in Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma is a common primary liver tumor with limited treatment options and poor prognosis. Changes in body composition (BC) have been shown to affect the prognosis of various types of tumors. Therefore, our study aimed to investigate the correlation between BC and clinical and oncological outcomes in patients with iCCA. All patients with iCCA who had surgery from 2010 to 2022 at our institution were included. We used CT scans and 3D Slicer software to assess BC and conducted logistic regressions as well as Cox regressions and Kaplan-Meier analyses to investigate associations between BC and clinical variables with focus on postoperative complications and oncological outcomes. BC was frequently altered in iCCA (n = 162), with 53.1% of the patients showing obesity, 63.2% sarcopenia, 52.8% myosteatosis, 10.1% visceral obesity, and 15.3% sarcopenic obesity. The multivariate analysis showed no meaningful association between BC and perioperative complications. Myosteatosis was associated with reduced overall survival (OS) in iCCA patients (myosteatosis vs. non-myosteatosis, 7 vs. 18 months, p = 0.016 log rank). Further, the subgroup analysis revealed a notable effect in the subset of R0-resected patients (myosteatosis vs. non-myosteatosis, 18 vs. 32 months, p = 0.025) and patients with nodal metastases (myosteatosis vs. non-myosteatosis, 7 vs. 18 months, p = 0.016). While altered BC is not associated with perioperative outcomes in iCCA, myosteatosis emerges as a prognostic factor for reduced OS in the overall and sub-populations of resected patients.

Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study

Background

Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs.

Methods

Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of the area under the curve (AUC).

Findings

The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84-0.92; DFS, p < 0.05) and Center 2 (AUC, 0.91; 95% CI: 0.85-0.97; DFS, p < 0.05). The clinical factors of vascular invasion, perineural invasion, and tumor grade were associated with LNM (p < 0.05). The combination nomogram showed better prediction capability for LNM (AUC, 0.93; 95% CI: 0.89-0.96). Notably, our model maintained a satisfactory predictive ability for tumors at the 2-cm threshold, demonstrating its effectiveness across different tumor sizes in Center 1 (≤2 cm: AUC, 0.90 and >2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91).

Interpretation

Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies.

Funding

This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).