Dataset Information

Predictive Value of ¹⁸F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer.

ABSTRACT: We investigated predictions from ¹⁸F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent ¹⁸F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outcome of the pathological complete response (pCR). The predictive accuracy of the ML model for the pCR was 93.4%. The accuracy of predicting pCR using the conventional PET parameters was up to 70.9%, and the accuracy of the physicians' assessment was 80.5%. The accuracy of the prediction from the ML model was significantly higher than those derived from conventional PET parameters and provided by physicians (p < 0.05). The ML model is useful for predicting pCR after neoadjuvant CCRT, which showed a higher predictive accuracy than those achieved from conventional PET parameters and from physicians.

SUBMITTER: Yoo J

PROVIDER: S-EPMC9031866 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer.

Yoo Jang J Lee Jaeho J Cheon Miju M Woo Sang-Keun SK Ahn Myung-Ju MJ Pyo Hong Ryull HR Choi Yong Soo YS Han Joung Ho JH Choi Joon Young JY

Cancers 20220414 8

We investigated predictions from 18F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent 18F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the ...[more]

PMID: 35454899

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Project description:ObjectiveThe objective of this study was to assess the clinical significance of increased fluorine-18-fluorodeoxyglucose (F-FDG) uptake on PET/CT in joints for evaluation of symptomatic osteoarthritis (OA) and prediction of progression.Patients and methodsIn this prospective study, shoulder, hip, and knee joints were imaged in 65 patients undergoing routine F-FDG PET/CT imaging. Patients completed the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire to assess joint pain, stiffness, and physical function. Standardized uptake values (SUVs) were measured in hip, knee, acromioclavicular (AC), and glenohumeral (GH) joints. Scout PET/CT images were evaluated for OA using the Kellgren and Lawrence (K/L) system. Patients were followed-up for 5 years to determine the progression of OA on the basis of follow-up imaging or surgical intervention.ResultsSUV of knee (r=0.309, P=0.0003), hip (r=0.260, P=0.0027), AC (r=0.186, P=0.0313), and GH (r=0.191, P=0.0271) joints correlated with WOMAC overall scores. Furthermore, SUV of knee (r=0.410, P<0.0001), hip (r=0.203, P=0.0199), and AC (r=0.364, P<0.0001) joints correlated with K/L scores. The area under the receiver operating characteristic curves for SUV were 0.734 (knee), 0.678 (hip), 0.661 (AC), and 0.544 (GH) for symptomatic OA detection based on WOMAC overall z-score greater or equal to 2. Compared with K/L score [hazard ratio (HR)=0.798, P=0.5324], age (HR=0.992, P=0.8978), and WOMAC overall score (HR=1.089, P=0.1265), only SUV (HR=5.653, P=0.0229) was an independent predictor of OA progression in the knees.ConclusionF-FDG PET/CT may be helpful with localization of painful abnormalities in the inflamed regions of the joints, which could potentially be used to direct individualized treatment in moderate and severe OA. Furthermore, SUV measurement on F-FDG PET/CT could serve as an inflammation activity index in the knees that may be predictive of outcomes and progression rate of OA.

Project description:We extracted image features from serial 18F-labeled fluorodeoxyglucose (FDG) positron emission tomography (PET) / computed tomography (CT) scans of anal cancer patients for the prediction of tumor recurrence after chemoradiation therapy (CRT). Seventeen patients (4 recurrent and 13 non-recurrent) underwent three PET/CT scans at baseline (Pre-CRT), in the middle of the treatment (Mid-CRT) and post-treatment (Post-CRT) were included. For each patient, Mid-CRT and Post-CRT scans were aligned to Pre-CRT scan. Comprehensive image features were extracted from CT and PET (SUV) images within manually delineated gross tumor volume, including geometry features, intensity features and texture features. The difference of feature values between two time points were also computed and analyzed. We employed univariate logistic regression model, multivariate model, and naïve Bayesian classifier to analyze the image features and identify useful tumor recurrent predictors. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the accuracy of the prediction. In univariate analysis, six geometry, three intensity, and six texture features were identified as significant predictors of tumor recurrence. A geometry feature of Roundness between Post-CRT and Pre-CRT CTs was identified as the most important predictor with an AUC value of 1.00 by multivariate logistic regression model. The difference of Number of Pixels on Border (geometry feature) between Post-CRT and Pre-CRT SUVs and Elongation (geometry feature) of Post-CRT CT were identified as the most useful feature set (AUC = 1.00) by naïve Bayesian classifier. To investigate the early prediction ability, we used features only from Pre-CRT and Mid-CRT scans. Orientation (geometry feature) of Pre-CRT SUV, Mean (intensity feature) of Pre-CRT CT, and Mean of Long Run High Gray Level Emphasis (LRHGLE) (texture feature) of Pre-CRT CT were identified as the most important feature set (AUC = 1.00) by multivariate logistic regression model. Standard deviation (intensity feature) of Mid-CRT SUV and difference of Mean of LRHGLE (texture feature) between Mid-CRT and Pre-CRT SUVs were identified as the most important feature set (AUC = 0.86) by naïve Bayesian classifier. The experimental results demonstrated the potential of serial PET/CT scans in early prediction of anal tumor recurrence.

Project description:PurposePolymyalgia rheumatica (PMR) can be difficult to diagnose. Whole-body [18F]FDG-PET/CT allows for a comprehensive evaluation of all relevant articular and extra-articular structures affected by PMR. We aimed to summarize current evidence on the diagnostic value of [18F]FDG-PET/CT for a diagnosis of PMR.MethodsPubMed/MEDLINE and the Cochrane Library database were searched from inception through May 31, 2020. Studies containing patients with PMR who underwent [18F]FDG-PET/CT were included. Screening and full-text review were performed by 3 investigators and data extraction by 2 investigators. Risk of bias was examined with the QUADAS-2 tool. Diagnostic test meta-analysis was performed with a bivariate model.ResultsTwenty studies were included in the systematic review, of which 9 studies (n = 636 patients) were eligible for meta-analysis. [18F]FDG positivity at the following sites was associated with a diagnosis of PMR: interspinous bursae (positive likelihood ratio (LR+) 4.00; 95% CI 1.84-8.71), hips (LR+ 2.91; 95% CI 2.09-4.05), ischial tuberosities (LR+ 2.86; 95% CI 1.91-4.28), shoulders (LR+ 2.57; 95% CI 1.24-5.32) and sternoclavicular joints (LR+ 2.31; 95% CI 1.33-4.02). Negative likelihood ratios (LR-) for these sites, as well as the greater trochanters, were all less than 0.50. Composite [18F]FDG-PET/CT scores, as reported in 3 studies, provided a pooled LR+ of 3.91 (95% CI 2.42-6.32) and LR- of 0.19 (95% CI 0.10-0.36). Moderate to high heterogeneity was observed across the studies, mainly due to differences in patient selection, scanning procedures and/or interpretation criteria.ConclusionSignificant [18F]FDG uptake at a combination of anatomic sites is informative for a diagnosis of PMR. [18F]FDG-PET/CT might be an important diagnostic tool in patients with suspected PMR. This study also highlights the need for adherence to published procedural recommendations and standardized interpretation criteria for the use of [18F]FDG-PET/CT in PMR.

Dataset Information

Predictive Value of ¹⁸F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer.

Publications

Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer.

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Dataset Information

Predictive Value of 18F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer.

Publications

Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Predictive Value of ¹⁸F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer.