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The purpose of this prospective study was to evaluate yttrium-90 glass microsphere treatment of unresectable liver metastases by fluorine-18 fluorodeoxyglucose positron emission tomography ([18F]FDG PET), and to compare the effectiveness of [18F]FDG PET for this purpose with that of computed tomography (CT) or magnetic resonance imaging (MRI) and determination of the serum carcinoembryonic antigen (CEA) level. Thirteen hepatic lobes from eight consecutive patients with colorectal cancer referred for 90Y-glass microsphere treatment of unresectable liver metastases who underwent both baseline (pretreatment) and 3-month posttreatment PET were studied. All patients also had correlative pre- and posttreatment CT or MRI for evaluation of the anatomic response and serum CEA determination for assessment of the total tumor load, as well as pretreatment hepatic intra-arterial technetium-99m macroaggregated albumin scan for lung shunting evaluation and hepatic arteriography for assessment of vascular anatomy and treatment. 90Y-glass microspheres were infused via an intra-arterial catheter under low pressure. Dedicated whole-body PET scans were analyzed visually and compared by lesion and by lobe with CT or MRI. A metabolic response after 90Y treatment to single or both hepatic lobes, assessed by PET, was present in a significantly higher proportion of the lobes than was an anatomic response, evaluated by CT or MRI (12 vs 2 lobes respectively, P<0.0002). Posttreatment PET showed no, stable, progressive, and new extrahepatic metastases in two, three, one, and two patients respectively. Following treatment, serum CEA decreased significantly, correlating with PET but not with CT or MRI. Thus, the study demonstrated a significant difference between the metabolic and the anatomic response after 90Y-glass microsphere treatment for unresectable liver metastases in colorectal cancer. PET appears to be an accurate indicator of treatment response.

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Purpose In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P < 0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.

PurposeThe aims were to determine if the maximum standardized uptake value (SUVmax) of the primary tumor as determined by preoperative 18F-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET) is an independent predictor of overall survival and to assess its prognostic value after stratification according to pathological staging.MethodsA retrospective clinicopathologic review of 363 patients who had a preoperative 18F-FDG PET done before undergoing attempted curative resection for early-stage (I & II) non-small cell lung cancer (NSCLC) was performed. Patients who had received any adjuvant or neoadjuvant chemotherapy or radiation therapy were excluded. The primary outcome measure was duration of overall survival. Receiver-operating characteristic (ROC) curves were plotted to find out the optimal cutoff values of SUVmax yielding the maximal sensitivity plus specificity for predicting the overall survival. Survival curves stratified by median SUVmax and optimal cutoff SUVmax were estimated by the Kaplan-Meier method and statistical differences were assessed using the log-rank test. Multivariate proportional hazards (Cox) regression analyses were applied to test the SUVmax’s independency of other prognostic factors for the prediction of overall survival.ResultsThe median duration of follow-up was 981 days (2.7 years). The median SUVmax was 5.9 for all subjects, 4.5 for stage IA, 8.4 for stage IB, and 10.9 for stage IIB. The optimal cutoff SUVmax was 8.2 for all subjects. No optimal cutoff could be established for specific stages. In univariate analyses, each doubling of SUVmax [i.e., each log (base 2) unit increase in SUVmax] was associated with a 1.28-fold [95% confidence interval (CI): 1.03–1.59, p = 0.029] increase in hazard of death. Univariate analyses did not show any significant difference in survival by SUVmax when data were stratified according to pathological stage (p = 0.119, p = 0.818, and p = 0.882 for stages IA, IB, and IIB, respectively). Multivariate analyses demonstrated that SUVmax was not an independent predictor of overall survival (p > 0.05).ConclusionEach doubling of SUVmax as determined by preoperative PET is associated with a 1.28-fold increase in hazard of death in early-stage (I & II) NSCLC. Preoperative SUVmax is not an independent predictor of overall survival.

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