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A letter to the editor is presented on visualization of primary uveal melanoma with positron emission tomography/computerized tomography scan.

Purpose: Radiotherapy is integral in the management of hematological malignancies (HM). Standard radiotherapy dose fractionation regimens range between 20 and 50 Gy in 10-25 fractions over 2-5 weeks. This study presents the outcomes of patients with HM treated with hypofractionation radiotherapy (HFRT) during the COVID-19 pandemic., Methods: Patients (n = 36) were treated with HFRT between January 2020 and September 2022. The outcomes measured were the overall response rate (ORR), freedom from local progression (FFLP), and overall survival (OS)., Results: The median follow-up was 13.2 months. Thirty-three patients (92%) had non-Hodgkin (NHL) or Hodgkin lymphoma (HL). Eighteen patients (50%) had aggressive and nine (25%) had indolent NHL. Nineteen patients (53%) presented with stage I/II and fifteen (42%) with stage III/IV disease. Twenty-five (69.4%) and eleven (30%) received consolidative and definitive RT, respectively. Twenty patients (56%) received treatment to the neck and/or thorax and nine (25%) to the abdomen or pelvis. The total dose ranged from 18 to 42.5 Gy in 6-17 fractions/2.67-5 Gy per fraction. The median dose in 2 Gy fractions for an alpha/beta (α/β) ratio of 10 amounted to 39 Gy (SD ± 13.86) and 43.6 Gy (SD ± 12) for an α/β of 3. The most commonly used fractionation scheme was 39 Gy in 13 fractions. ORR was 94.4% for the entire cohort, and 100, 94.4, and 83.3% for indolent NHL, aggressive NHL, and HL patients. The two-year FFLP was 76% (95% CI: 34-93%) for the entire cohort and 100, 87 (95% CI: 56.4-96.5%), and 42% (95% CI: 1.1-84.3%) for the indolent NHL, aggressive NHL, and HL patients. Two-year OS for the entire cohort was 80% (95% CI: 59.9-90.5%) and 100, 66.1 (95% CI: 36.4-84.4%), and 100% for the indolent NHL, aggressive NHL, and HL patients. Only one patient presented with grade two pulmonary toxicity., Conclusions: HFRT in HM provides excellent local control to be validated in a larger prospective study.

Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson's Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.

Objective: To ascertain whether fluorodeoxyglucose positron emission tomography is indicated for clinical staging of superficial cancer, we sought to determine if it accurately classifies tumor (T), regional nodal (N), and distant metastases (M), including distinguishing high-grade dysplasia (Tis) from invasive cancer (T1)., Methods: Fifty-eight superficial esophageal cancer patients had preoperative positron emission tomography, 53 (91%) fused with computed tomography. Tumor characteristics, esophagoscopy findings, and pTNM were compared with positron emission tomography cTNM. pT1 was subdivided into intramucosal cancers with lamina propria or muscularis mucosa invasion and submucosal cancers with inner or outer invasion., Results: Fluorodeoxyglucose uptake increased with pT, from 5/11 (45%) for pTis to 11/16 (69%) for pT1 (outer submucosa), P=0.07, as it did for standardized uptake value, median 0 for pTis to 2.7 for pT1 (outer submucosa), P=0.06. Positron emission tomography could not differentiate Tis (5/11, 45%) from T1 (26/47, 55%; P=0.03). Regional nodal fluorodeoxyglucose uptake in three patients (standardized uptake value 2.8, 4.9, 11) was false positive; in six pN1 patients, it was false negative. Positron emission tomography had 0% sensitivity and positive predictive value for N1. There were no distant metastases; one patient developed a pulmonary metastasis 15 months postoperatively. Positron emission tomography detected three (5%) distant hypermetabolic sites, all synchronous tumors (papillary thyroid cancer, adrenal pheochromocytoma, rectal adenoma). Only increasing tumor length was related to greater fluorodeoxyglucose uptake (P=0.004) and higher standardized uptake value (P=0.001)., Conclusions: Because positron emission tomography can neither differentiate pTis from T1 nor classify T, N, and M, it is not indicated in staging superficial esophageal cancer. Finding a synchronous primary tumor in approximately every 20th patient is its only benefit. Better, less expensive screening tools are available for common synchronous malignancies.

Unlabelled: Registration and fusion of whole-body functional PET and anatomic CT is significant for accurate differentiation of viable tumors from benign masses, radiotherapy planning and monitoring treatment response, and cancer staging. Whole-body PET and CT acquired on separate scanners are misregistered because of differences in patient positions and orientations, couch shapes, and breathing protocols. Although a combined PET/CT scanner removes many of these misalignments, breathing-related nonrigid mismatches still persist., Methods: We have developed a new, fully automated normalized mutual information-based 3-dimensional elastic image registration technique that can accurately align whole-body PET and CT images acquired on stand-alone scanners as well as a combined PET/CT scanner. The algorithm morphs the PET image to align spatially with the CT image by generating an elastic transformation field by interpolating quaternions and translations from multiple 6-parameter rigid-body registrations, each obtained for hierarchically subdivided image subvolumes. Fifteen whole-body (spanning thorax and abdomen) PET/CT image pairs acquired separately and 5 image pairs acquired on a combined scanner were registered. The cases were selected on the basis of the availability of both CT and PET images, without any other screening criteria, such as a specific clinical condition or prognosis. A rigorous quantitative validation was performed by evaluating algorithm performance in the context of variability among 3 clinical experts in the identification of up to 32 homologous anatomic landmarks., Results: The average execution time was 75 and 45 min for images acquired using separate scanners and combined scanner, respectively. Visual inspection indicated improved matching of homologous structures in all cases. The mean registration accuracy (5.5 and 5.9 mm for images from separate scanners and combined scanner, respectively) was found comparable to the mean interexpert difference in landmark identification (5.6 +/- 2.4 and 6.6 +/- 3.4 mm, respectively). The variability in landmark identification did not show statistically significant changes on replacing any expert by the algorithm., Conclusion: We have presented a new and automated elastic registration algorithm to correct for nonrigid misalignments in whole-body PET/CT images as well as improve the "mechanical" registration of a combined PET/CT scanner. The algorithm performance was on par with the average opinion of 3 experts.