The impact of computed high b-value images on the diagnostic accuracy of DWI for prostate cancer: A receiver operating characteristics analysis.

To evaluate the performance of computed high b value diffusion-weighted images (DWI) in prostate cancer detection. 97 consecutive patients who had undergone multiparametric MRI of the prostate followed by biopsy were reviewed. Five radiologists independently scored 138 lesions on native high b-value images (b = 1200 s/mm2), apparent diffusion coefficient (ADC) maps, and computed high b-value images (contrast equivalent to b = 2000 s/mm2) to compare their diagnostic accuracy. Receiver operating characteristic (ROC) analysis and McNemar's test were performed to assess the relative performance of computed high b value DWI, native high b-value DWI and ADC maps. No significant difference existed in the area under the curve (AUC) for ROCs comparing B1200 (b = 1200 s/mm2) to computed B2000 (c-B2000) in 5 readers. In 4 of 5 readers c-B2000 had significantly increased sensitivity and/or decreased specificity compared to B1200 (McNemar's p < 0.05), at selected thresholds of interpretation. ADC maps were less accurate than B1200 or c-B2000 for 2 of 5 readers (P < 0.05). This study detected no consistent improvement in overall diagnostic accuracy using c-B2000, compared with B1200 images. Readers detected more cancer with c-B2000 images (increased sensitivity) but also more false positive findings (decreased specificity)

Elevated [11C]-D-Deprenyl Uptake in Chronic Whiplash Associated Disorder Suggests Persistent Musculoskeletal Inflammation

There are few diagnostic tools for chronic musculoskeletal pain as structural imaging methods seldom reveal pathological alterations. This is especially true for Whiplash Associated Disorder, for which physical signs of persistent injuries to the neck have yet to be established. Here, we sought to visualize inflammatory processes in the neck region by means Positron Emission Tomography using the tracer 11C-D-deprenyl, a potential marker for inflammation. Twenty-two patients with enduring pain after a rear impact car accident (Whiplash Associated Disorder grade II) and 14 healthy controls were investigated. Patients displayed significantly elevated tracer uptake in the neck, particularly in regions around the spineous process of the second cervical vertebra. This suggests that whiplash patients have signs of local persistent peripheral tissue inflammation, which may potentially serve as a diagnostic biomarker. The present investigation demonstrates that painful processes in the periphery can be objectively visualized and quantified with PET and that 11C-D-deprenyl is a promising tracer for these purposes

Engineering Luciferases for Assays and Imaging

Luciferases have served a number of purposes in biomedical applications, including within reporter gene and split reporter complementation assays. These proteins, however, have not evolved for the purpose of biomedical research, and it is not surprising that the utility and robustness of these assays can be improved by protein engineering of the luciferase. In this chapter, we provide an overview of luciferases, protein engineering, and how protein engineering is applied to luciferases

The use of PET/MRI for imaging rectal cancer

Combined PET/MRI is a proposed imaging modality for rectal cancer, leveraging the advantages of MRI and 18F-fluorodeoxyglucose PET. Rectal cancer PET/MRI protocols typically include dedicated pelvis bed positions utilizing small field-of-view T2-weighted imaging. For staging of the primary tumor, PET/MRI can help delineate the extent of tumor better as well as the extent of tumor beyond the muscularis propria. PET uptake may help characterize small lymph nodes, and the use of hepatobiliary phase imaging can improve the detection of small hepatic metastases. The most beneficial aspect of PET/MRI may be in treatment response, although current data are limited on how to combine PET and MRI data in this setting. Limitations of PET/MRI include the inability to detect small pulmonary nodules and issues related to attenuation correction, although the development of new attenuation correction techniques may address this issue. Overall PET/MRI can improve the staging of rectal cancer, although this potential has yet to be fulfilled

Multimodality imaging of tumor xenografts and metastases in mice with combined small-animal PET, small-animal CT, and bioluminescence imaging

Recent developments have established molecular imaging of mouse models with small-animal PET and bioluminescence imaging (BLI) as an important tool in cancer research. One of the disadvantages of these imaging modalities is the lack of anatomic information. We combined small-animal PET and BLI technology with small-animal CT to obtain fusion images with both molecular and anatomic information. Methods: We used small-animal PET/CT and BLI to detect xenografts of different cell lines and metastases of a melanoma cell line (A375M-3F) that had been transduced with a lentiviral vector containing a trimodality imaging reporter gene encoding a fusion protein with Renilla luciferase, monomeric red fluorescent protein, and a mutant herpes simplex virus type 1 thymidine kinase. Results: Validation studies in mouse xenograft models showed a good coregistration of images from both PET and CT. Melanoma metastases were detected by F-18-FDG PET, 9-[4-F-18-fluoro-3-(hydroxymethyl) butyl]guanine (F-18-FHBG) PET, CT, and BLI and confirmed by ex vivo assays of Renilla luciferase and mutant thymidine kinase expression. F-18-FHBG PET/CT allowed detection and localization of lesions that were not seen on CT because of poor contrast resolution and were not seen on F-18-FDG PET because of higher background uptake relative to F-18-FHBG. Conclusion: The combination of F-18-FHBG PET, small-animal CT, and BLI allows a sensitive and improved quantification of tumor burden in mice. This technique is potentially useful for the study of the biologic determinants of metastasis and for the evaluation of novel cancer treatments.status: publishe

Radiology Decision Support System for Selecting Appropriate CT Imaging Titles Using Machine Learning Techniques Based on Electronic Medical Records

Radiologists use an imaging order from the ordering physician, which includes a radiology title, to select the most suitable imaging protocol. Inappropriate radiology titles can disrupt protocol selection and result in mistaken or delayed diagnosis. The objective of this work is to develop an algorithm to predict correct radiology titles from incoming exam order data. The proposed instrument is an ensemble of five decision tree-based machine learning (ML) techniques (Light Gradient Boosting Machine, eXtreme Gradient Boosting Machine, Random Forest, Adaptive Boosting, and Random UnderSampling Boosting Model) trained to recommend radiology titles of computed tomography imaging examinations based on electronic medical records. Issues of imbalanced data and generalization were addressed. The tuned models were used to predict the top three radiology titles for the radiologist revision. The models were evaluated using a 10-fold cross-validation method, yielding an approximate average accuracy of $80.5\% \pm 2.02\%$ and F1-score of $80.3\% \pm 1.67\%$ for all models, while the ensemble classifier (~83% F1-score) outperformed individual models. An accumulated average accuracy of ~92% was obtained for the top three predictions. ML techniques can predict radiology titles and identify highly important features. The proposed system can guide physicians toward selecting appropriate radiology titles and alert radiologists to inconsistencies between the radiology title in the exam order and the patient’s underlying conditions, thereby improving imaging utility and increasing diagnostic accuracy, which favors better patient outcomes