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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.

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