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Vogele D, Mueller T, Wolf D, Otto S, Manoj S, Goetz M, Ettrich TJ, Beer M. Applicability of the CT Radiomics of Skeletal Muscle and Machine Learning for the Detection of Sarcopenia and Prognostic Assessment of Disease Progression in Patients with Gastric and Esophageal Tumors. Diagnostics (Basel) 2024; 14:198. [PMID: 38248074 PMCID: PMC10814393 DOI: 10.3390/diagnostics14020198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/10/2024] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
PURPOSE Sarcopenia is considered a negative prognostic factor in patients with malignant tumors. Among other diagnostic options, computed tomography (CT), which is repeatedly performed on tumor patients, can be of further benefit. The present study aims to establish a framework for classifying the impact of sarcopenia on the prognosis of patients diagnosed with esophageal or gastric cancer. Additionally, it explores the significance of CT radiomics in both diagnostic and prognostic methodologies. MATERIALS AND METHODS CT scans of 83 patients with esophageal or gastric cancer taken at the time of diagnosis and during a follow-up period of one year were evaluated retrospectively. A total of 330 CT scans were analyzed. Seventy three of these patients received operative tumor resection after neoadjuvant chemotherapy, and 74% of the patients were male. The mean age was 64 years (31-83 years). Three time points (t) were defined as a basis for the statistical analysis in order to structure the course of the disease: t1 = initial diagnosis, t2 = following (neoadjuvant) chemotherapy and t3 = end of the first year after surgery in the "surgery" group or end of the first year after chemotherapy. Sarcopenia was determined using the psoas muscle index (PMI). The additional analysis included the analysis of selected radiomic features of the psoas major, quadratus lumborum, and erector spinae muscles at the L3 level. Disease progression was monitored according to the response evaluation criteria in solid tumors (RECIST 1.1). CT scans and radiomics were used to assess the likelihood of tumor progression and their correlation to sarcopenia. For machine learning, the established algorithms decision tree (DT), K-nearest neighbor (KNN), and random forest (RF) were applied. To evaluate the performance of each model, a 10-fold cross-validation as well as a calculation of Accuracy and Area Under the Curve (AUC) was used. RESULTS During the observation period of the study, there was a significant decrease in PMI. This was most evident in patients with surgical therapy in the comparison between diagnosis and after both neoadjuvant therapy and surgery (each p < 0.001). Tumor progression (PD) was not observed significantly more often in the patients with sarcopenia compared to those without sarcopenia at any time point (p = 0.277 to p = 0.465). On average, PD occurred after 271.69 ± 104.20 days. The time from initial diagnosis to PD in patients "with sarcopenia" was not significantly shorter than in patients "without sarcopenia" at any of the time points (p = 0.521 to p = 0.817). The CT radiomics of skeletal muscle could predict both sarcopenia and tumor progression, with the best results for the psoas major muscle using the RF algorithm. For the detection of sarcopenia, the Accuracy was 0.90 ± 0.03 and AUC was 0.96 ± 0.02. For the prediction of PD, the Accuracy was 0.88 ± 0.04 and the AUC was 0.93 ± 0.04. CONCLUSIONS In the present study, the CT radiomics of skeletal muscle together with machine learning correlated with the presence of sarcopenia, and this can additionally assist in predicting disease progression. These features can be classified as promising alternatives to conventional methods, with great potential for further research and future clinical application. However, when sarcopenia was diagnosed with PMI, no significant correlation between sarcopenia and PD could be observed.
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Affiliation(s)
- Daniel Vogele
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
| | - Teresa Mueller
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
| | - Daniel Wolf
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- Visual Computing Group, Institute for Media Informatics, Ulm University, 89081 Ulm, Germany
- XAIRAD—Artificial Intelligence in Experimental Radiology, University Hospital of Ulm, 89081 Ulm, Germany
| | - Stephanie Otto
- Comprehensive Cancer Center Ulm (CCCU), Ulm University Medical Center, 89081 Ulm, Germany;
| | - Sabitha Manoj
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- XAIRAD—Artificial Intelligence in Experimental Radiology, University Hospital of Ulm, 89081 Ulm, Germany
| | - Michael Goetz
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- XAIRAD—Artificial Intelligence in Experimental Radiology, University Hospital of Ulm, 89081 Ulm, Germany
| | - Thomas J. Ettrich
- Department of Internal Medicine I, Ulm University Medical Center, 89081 Ulm, Germany;
- i2SouI—Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, 89081 Ulm, Germany
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, 89081 Ulm, Germany; (T.M.); (D.W.); (S.M.); (M.G.); (M.B.)
- i2SouI—Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, 89081 Ulm, Germany
- MoMan—Center for Translational Imaging, Department of Internal Medicine II, University Hospital of Ulm, 89081 Ulm, Germany
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Liu Z, Wu Y, Khan AA, Lun LU, Wang J, Chen J, Jia N, Zheng S, Xu X. Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma. J Zhejiang Univ Sci B 2024; 25:83-90. [PMID: 38163668 PMCID: PMC10758209 DOI: 10.1631/jzus.b2300363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/25/2023] [Indexed: 01/03/2024]
Abstract
Hepatocellular carcinoma (HCC) is one of the most common malignancies and is a major cause of cancer-related mortalities worldwide (Forner et al., 2018; He et al., 2023). Sarcopenia is a syndrome characterized by an accelerated loss of skeletal muscle (SM) mass that may be age-related or the result of malnutrition in cancer patients (Cruz-Jentoft and Sayer, 2019). Preoperative sarcopenia in HCC patients treated with hepatectomy or liver transplantation is an independent risk factor for poor survival (Voron et al., 2015; van Vugt et al., 2016). Previous studies have used various criteria to define sarcopenia, including muscle area and density. However, the lack of standardized diagnostic methods for sarcopenia limits their clinical use. In 2018, the European Working Group on Sarcopenia in Older People (EWGSOP) renewed a consensus on the definition of sarcopenia: low muscle strength, loss of muscle quantity, and poor physical performance (Cruz-Jentoft et al., 2019). Radiological imaging-based measurement of muscle quantity or mass is most commonly used to evaluate the degree of sarcopenia. The gold standard is to measure the SM and/or psoas muscle (PM) area using abdominal computed tomography (CT) at the third lumbar vertebra (L3), as it is linearly correlated to whole-body SM mass (van Vugt et al., 2016). According to a "North American Expert Opinion Statement on Sarcopenia," SM index (SMI) is the preferred measure of sarcopenia (Carey et al., 2019). The variability between morphometric muscle indexes revealed that they have different clinical relevance and are generally not applicable to broader populations (Esser et al., 2019).
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Affiliation(s)
- Zhikun Liu
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou 310003, China
| | - Yichao Wu
- Zhejiang University School of Medicine, Hangzhou 310058, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou 310003, China
| | - Abid Ali Khan
- Zhejiang University School of Medicine, Hangzhou 310058, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou 310003, China
| | - L U Lun
- Department of Radiology, Easter Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China
| | - Jianguo Wang
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou 310003, China
| | - Jun Chen
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou 310003, China
| | - Ningyang Jia
- Department of Radiology, Easter Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai 200438, China.
| | - Shusen Zheng
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou 310003, China.
- Department of Hepatobiliary and Pancreatic Surgery, Shulan (Hangzhou) Hospital, Hangzhou 310022, China.
| | - Xiao Xu
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
- Zhejiang University School of Medicine, Hangzhou 310058, China.
- NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou 310003, China.
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van der Kroft G, Wee L, Rensen SS, Brecheisen R, van Dijk DPJ, Eickhoff R, Roeth AA, Ulmer FT, Dekker A, Neumann UP, Olde Damink SWM. Identifying radiomics signatures in body composition imaging for the prediction of outcome following pancreatic cancer resection. Front Oncol 2023; 13:1062937. [PMID: 37637046 PMCID: PMC10449585 DOI: 10.3389/fonc.2023.1062937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 06/26/2023] [Indexed: 08/29/2023] Open
Abstract
Background Computerized radiological image analysis (radiomics) enables the investigation of image-derived phenotypes by extracting large numbers of quantitative features. We hypothesized that radiomics features may contain prognostic information that enhances conventional body composition analysis. We aimed to investigate whether body composition-associated radiomics features hold additional value over conventional body composition analysis and clinical patient characteristics used to predict survival of pancreatic ductal adenocarcinoma (PDAC) patients. Methods Computed tomography images of 304 patients undergoing elective pancreatic cancer resection were analysed. 2D radiomics features were extracted from skeletal muscle and subcutaneous and visceral adipose tissue (SAT and VAT) compartments from a single slice at the third lumbar vertebra. The study population was randomly split (80:20) into training and holdout subsets. Feature ranking with Least Absolute Shrinkage Selection Operator (LASSO) followed by multivariable stepwise Cox regression in 1000 bootstrapped re-samples of the training data was performed and tested on the holdout data. The fitted regression predictors were used as "scores" for a clinical (C-Score), body composition (B-Score), and radiomics (R-Score) model. To stratify patients into the highest 25% and lowest 25% risk of mortality compared to the middle 50%, the Harrell Concordance Index was used. Results Based on LASSO and stepwise cox regression for overall survival, ASA ≥3 and age were the most important clinical variables and constituted the C-score, and VAT-index (VATI) was the most important body composition variable and constituted the B-score. Three radiomics features (SATI_original_shape2D_Perimeter, VATI_original_glszm_SmallAreaEmphasis, and VATI_original_firstorder_Maximum) emerged as the most frequent set of features and yielded an R-Score. Of the mean concordance indices of C-, B-, and R-scores, R-score performed best (0.61, 95% CI 0.56-0.65, p<0.001), followed by the C-score (0.59, 95% CI 0.55-0.63, p<0.001) and B-score (0.55, 95% CI 0.50-0.60, p=0.03). Kaplan-Meier projection revealed that C-, B, and R-scores showed a clear split in the survival curves in the training set, although none remained significant in the holdout set. Conclusion It is feasible to implement a data-driven radiomics approach to body composition imaging. Radiomics features provided improved predictive performance compared to conventional body composition variables for the prediction of overall survival of PDAC patients undergoing primary resection.
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Affiliation(s)
- Gregory van der Kroft
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Leonard Wee
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Sander S. Rensen
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Ralph Brecheisen
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
| | - David P. J. van Dijk
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Roman Eickhoff
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Anjali A. Roeth
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Florian T. Ulmer
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Ulf P. Neumann
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
| | - Steven W. M. Olde Damink
- Department of General, Gastrointestinal, Hepatobiliary and Transplant Surgery, RWTH Aachen University Hospital, European Surgical Center Aachen Maastricht (ESCAM), Aachen, Germany
- Department of Surgery, Maastricht University Medical Center, European Surgical Center Aachen Maastricht (ESCAM), Maastricht, Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
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Wang X, Zheng J, Yang H, Yang X, Cai W, Chen X, Zhang W, Shen X. Prognostic value of the preoperative albumin-bilirubin score among patients with stages I-III gastric cancer undergoing radical resection: A retrospective study. Clin Transl Sci 2023; 16:850-860. [PMID: 36762709 PMCID: PMC10175983 DOI: 10.1111/cts.13493] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/28/2022] [Accepted: 01/18/2023] [Indexed: 02/11/2023] Open
Abstract
The albumin-bilirubin (ALBI) score was originally used to accurately assess liver dysfunction and predict the prognoses of patients with hepatocellular carcinoma. Following its more recent application to patients with gastrointestinal tumors, this study analyzed the prognostic value of the ALBI score in Chinese patients with advanced resectable (tumor-node-metastasis [TNM] stages I-III) gastric cancer (GC). This study investigated 1185 patients with advanced GC, including 429 with TNM stage I. The patients were divided into training and verifications groups (593 and 592 patients, respectively) in which these patients were classified as high risk (ALBI score ≥ -2.65) or low risk (ALBI score < -2.65). Univariate and multivariate Cox regression analyses were performed, and a visual survival prediction model (nomogram) was created. On Kaplan-Meier analysis, patients who were low-risk and high-risk according to their ALBI scores had significantly different survival rates in both the training and verification groups (p < 0.01). The difference was also significant when analyzing only patients with TNM stage I GC (p = 0.031). Univariate and multivariate analyses showed that the ALBI score (p = 0.014), age (p < 0.001), Nutritional Risk Screening 2002 score (p = 0.024), sarcopenia (p = 0.049), tumor differentiation (p = 0.027), and TNM stage (p < 0.001) were independent risk factors for survival. Our survival prediction model that incorporated the ALBI score accurately predicted the 5-year survival rate of Chinese patients with GC. Therefore, the ALBI score is a valid clinical indicator and good predictor of survival after surgery for progressive GC. Moreover, this score is simple to derive and does not burden patients with additional costs.
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Affiliation(s)
- Xiang Wang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang, China
| | - Jingwei Zheng
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang, China
| | - Hui Yang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang, China
| | - Xinxin Yang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang, China
| | - Wentao Cai
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang, China
| | - Xiaodong Chen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Weiteng Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
| | - Xian Shen
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang, China.,Department of Gastrointestinal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China
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