The total lesion glycolysis in hypoxia (hTLG) was hMTV × FDG SUVmean. The extent of resection (EOR) involving cytoreduction surgery was volumetric change based on planimetry methods using MRI. These factors were tested for correlation with patient prognosis. 4Results:All tumor lesions were FMISO-positive and FDG-positive. Univariate analysis indicated that hMTV, hTLG, and EOR were significantly correlated with PFS (p=0.007, p=0.04, and p=0.01, respectively) and that hMTV, hTLG, and EOR were also significantly correlated with OS (p=0.0028, p=0.037, and p=0.014, respectively). In contrast, none of FDG TNR, FMISO TNR, GTV, HV, patients' age, or Karnofsky Performance Scale (KPS) was significantly correlated with PSF or OS. The hMTV and hTLG were found to be independent factors affecting PFS and OS on multivariate analysis. Conclusions:We introduced hMTV and hTLG using FDG and FMISO PET to define metabolically-active hypoxic volume. Univariate and multivariate analyses demonstrated that both hMTV and hTLG are significant predictors for PFS and OS in glioblastoma patients.

Background: As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detection systems on artificial intelligence (AI) to prevent human oversight and misdiagnosis are rapidly growing. We aimed to develop a convolutional neural network (CNN)-based system that can classify whole-body FDG PET as 1) benign, 2) malignant or 3) equivocal. Methods: This retrospective study investigated 3485 sequential patients with malignant or suspected malignant disease, who underwent whole-body FDG PET/CT at our institute. All the cases were classified into the 3 categories by a nuclear medicine physician. A residual network (ResNet)-based CNN architecture was built for classifying patients into the 3 categories. In addition, we performed a region-based analysis of CNN (head-and-neck, chest, abdomen, and pelvic region). Results: There were 1280 (37%), 1450 (42%), and 755 (22%) patients classified as benign, malignant and equivocal, respectively. In the patient-based analysis, CNN predicted benign, malignant and equivocal images with 99.4, 99.4, and 87.5% accuracy, respectively. In region-based analysis, the prediction was correct with the probability of 97.3% (head-and-neck), 96.6% (chest), 92.8% (abdomen) and 99.6% (pelvic region), respectively. Conclusion: The CNN-based system reliably classified FDG PET images into 3 categories, indicating that it could be helpful for physicians as a double-checking system to prevent oversight and misdiagnosis.

Influence of hot top and mould design on the formation of central porosities and loose structure in heavy forging ingot was analysed by using finite element method. The results of the analysis were compared with those of sectioning investigation of 100 and 135 t ingots and the influence of mould and hot top design on the internal defects has been made clear quantitatively. The result shows that the geometry of hot top and mould design plays most important role in the manufacture of sound heavy ingots. The central porosities and loose structure are liable to increase when the rate of vertical solidification at the centerline of ingot exceeds the value of about 10 mm/ min, and the defects are strengthened at the area where the rate of solidification is accelerated. For 0.25%C-3.5%Ni-Cr-Mo-V steel ingot, "A" segregation begins to form when the rate of transverse (horizontal) solidification decreases to the value of about 0.8 mm/min.

Both the intensity and the volume of tumor hypoxia rapidly decreased in the early phase of radiotherapy, indicating reoxygenation of the tumor hypoxia. In contrast, the FDG uptake declined gradually with the course of radiotherapy, indicating that the tumoricidal effect continues over the entire course of radiation treatment.

Background Hypoxia can induce radiation resistance and is an independent prognostic marker for outcome in head and neck cancer. As 18 F-FMISO (FMISO), a hypoxia tracer for PET, is far less common than 18 F-FDG (FDG) and two separate PET scans result in doubled cost and radiation exposure to the patient, we aimed to predict hypoxia from FDG PET with new techniques of voxel based analysis and texture analysis. Methods Thirty-eight patients with head-and-neck cancer underwent consecutive FDG and FMISO PET scans before any treatment. ROIs enclosing the primary cancer were compared in a voxel-by-voxel manner between FDG and FMISO PET. Tumour hypoxia was defined as the volume with a tumour-to-muscle ratio (TMR) > 1.25 in the FMISO PET and hypermetabolic volume was defined as >50% SUVmax in the FDG PET. The concordance rate was defined as percentage of voxels within the tumour which were both hypermetabolic and hypoxic. 38 different texture analysis (TA) parameters were computed based on the ROIs and correlated with presence of hypoxia. Results Within the hypoxic tumour regions, the FDG uptake was twice as high as in the non-hypoxic tumour regions (SUVmean 10.9 vs. 5.4; p<0.001). A moderate correlation between FDG and FMISO uptake was found by a voxel-by-voxel comparison (r = 0.664 p<0.001). The average concordance rate was 25% (± 22%). Entropy was the TA parameter showing the highest correlation with hypoxia (r = 0.524 p<0.001). Conclusion FDG uptake was higher in hypoxic tumour regions than in non-hypoxic regions as expected by tumour biology. A moderate correlation between FDG and FMISO PET was found by voxel-based analysis. TA yielded similar results in FDG and FMISO PET. However, it may not be possible to predict tumour hypoxia even with the help of texture analysis.

Our findings revealed that the DA is suitable to decide the threshold for the volume-based analysis. The fasting time was significantly associated with the cardiac FDG uptake.

From 1973 to 1989, surgical resection was performed in 235 stage IIIA non-small-cell lung cancer patients (78% of all admitted stage IIIA patients). Complete resection was accomplished in 155 patients and 80 underwent incomplete resection. The rate of incomplete resection was higher in patients with adenocarcinoma than in those with squamous cell carcinoma. There were 7 operative deaths (2.8%) among the patients undergoing operation. The five-year survival rate of the group having complete resection was 32%, whereas that of the incomplete resection group was 5% (p less than 0.05). The five-year survival rate of T3NO-1MO patients with complete resection was 50% and that of T1-2N2MO patients was 30%. However, the five-year survival rate of patients with T3N2MO disease was significantly poorer at 10% (p less than 0.05). The five-year survival rates of patients undergoing complete resection including the combined resection of an adjacent organ were: pericardium 43%; chest wall 43%; pleura 34%; and bronchus 46%. Forty-nine patients survived over three years and 10 of them died between three and five years after surgery, but five-year, four-year, and three-year survivors numbered 29, 4, and 6, respectively. Surgical resection appears to be the treatment of choice for stage IIIA non-small-cell lung cancer whenever complete resection is feasible.

Patient misidentification in imaging examinations has become a serious problem in clinical settings. Such misidentification could be prevented if patient characteristics such as sex, age, and body weight could be predicted based on an image of the patient, with an alert issued when a mismatch between the predicted and actual patient characteristic is detected. Here, we tested a simple convolutional neural network (CNN)-based system that predicts patient sex from FDG PET-CT images. This retrospective study included 6,462 consecutive patients who underwent whole-body FDG PET-CT at our institute. The CNN system was used for classifying these patients by sex. Seventy percent of the randomly selected images were used to train and validate the system; the remaining 30% were used for testing. The training process was repeated five times to calculate the system’s accuracy. When images for the testing were given to the learned CNN model, the sex of 99% of the patients was correctly categorized. We then performed an image-masking simulation to investigate the body parts that are significant for patient classification. The image-masking simulation indicated the pelvic region as the most important feature for classification. Finally, we showed that the system was also able to predict age and body weight. Our findings demonstrate that a CNN-based system would be effective to predict the sex of patients, with or without age and body weight prediction, and thereby prevent patient misidentification in clinical settings.