Purpose: This study aimed to investigate whether metabolic tumor volume (MTV) measured from [18 F]-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) predicts short-term outcome to radiotherapy with or without chemotherapy and disease-free survival (DFS) in patients with pharyngeal cancers. Experimental Design: The MTVs of primary sites with or without neck nodes were measured in 82 patients. Short-term outcome was assessed using the treatment response evaluation by the Response Evaluation Criteria in Solid Tumors and recurrence events during follow-up (complete response/no recurrence or residual disease/recurrence). Results: A total of 64 patients had complete response/no recurrence as of the last follow-up. A cutoff of 40 mL for the MTV was the best discriminative value for predicting treatment response. By univariate analyses, patients with MTV >40 mL showed a significantly lower number of complete response/no recurrence than did patients with MTV ≤40 mL [68.2% versus 87.8%; hazard ratio (HR), 3.34; 95% confidence interval (95% CI), 1.09-10.08; P = 0.03], as is the same in tumor-node-metastasis stage (87.5% for I-II versus 90% for III versus 63.8% for IV; P = 0.02). However, MTV was only a significant predictor of short-term outcome by multivariate analyses (HR, 4.09; 95% CI, 1.02-16.43; P = 0.04). MTV >40 mL indicated a significantly worse DFS than MTV ≤40 mL (HR, 3.42; 95% CI, 1.04-11.26;P = 0.04). The standardized uptake value for the primary tumor did not show any correlation with treatment outcome or DFS. Conclusion: MTV has a potential value in predicting short-term outcome and DFS in patients with pharyngeal cancers. (Clin Cancer Res 2009;15(18):5861-8)

MTV, a volumetric parameter of (18)F-FDG PET, is an important independent prognostic factor for survival and a better predictor of survival than SUVmax for the primary tumor in patients with esophageal carcinoma.

Accurate prediction of cancer prognosis before the start of treatment is important since these predictions often affect the choice of treatment. Prognosis is usually based on anatomical staging and other clinical factors. However, the conventional system is not sufficient to accurately and reliably determine prognosis. Metabolic parameters measured by 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) have the potential to provide valuable information regarding prognosis and treatment response evaluation in cancer patients. Among these parameters, volume-based PET parameters such as metabolic tumor volume and total lesion glycolysis are especially promising. However, the measurement of these parameters is significantly affected by the imaging methodology and specific image characteristics, and a standard method for these parameters has not been established. This review introduces volume-based PET parameters as potential prognostic indicators, and highlights methodological considerations for measurement, potential implications, and prospects for further studies.

Volume-based parameters of (18)F-FDG PET/CT have the potential to provide prognostic information in MPM patients who are receiving surgery or palliative chemotherapy.

The volume-based parameter of PET is an independent prognostic factor for survival in addition to pathological tumor-node-metastasis stage and a promising tool for better prediction of outcome in patients with early-stage NSCLC.

The volume-based PET parameters (MTV and TLG) are significant prognostic factors for survival independent of tumour stage and better prognostic imaging biomarkers than SUVmax in patients with stage IIIA NSCLC after surgical resection.

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Purpose We sought to distinguish lung adenocarcinoma (ADC) from squamous cell carcinoma using a machine-learning algorithm with PET-based radiomic features. Methods A total of 396 patients with 210 ADCs and 186 squamous cell carcinomas who underwent FDG PET/CT prior to treatment were retrospectively analyzed. Four clinical features (age, sex, tumor size, and smoking status) and 40 radiomic features were investigated in terms of lung ADC subtype prediction. Radiomic features were extracted from the PET images of segmented tumors using the LIFEx package. The clinical and radiomic features were ranked, and a subset of useful features was selected based on Gini coefficient scores in terms of associations with histological class. The areas under the receiver operating characteristic curves (AUCs) of classifications afforded by several machine-learning algorithms (random forest, neural network, naive Bayes, logistic regression, and a support vector machine) were compared and validated via random sampling. Results We developed and validated a PET-based radiomic model predicting the histological subtypes of lung cancer. Sex, SUVmax, gray-level zone length nonuniformity, gray-level nonuniformity for zone, and total lesion glycolysis were the 5 best predictors of lung ADC. The logistic regression model outperformed all other classifiers (AUC = 0.859, accuracy = 0.769, F1 score = 0.774, precision = 0.804, recall = 0.746) followed by the neural network model (AUC = 0.854, accuracy = 0.772, F1 score = 0.777, precision = 0.807, recall = 0.750). Conclusions A machine-learning approach successfully identified the histological subtypes of lung cancer. A PET-based radiomic features may help clinicians improve the histopathologic diagnosis in a noninvasive manner.