PURPOSE: To prospectively evaluate magnetic resonance (MR) imaging and MR spectroscopy for depiction of local prostate cancer recurrence after external-beam radiation therapy, with step-section pathologic findings as the standard of reference. MATERIALS AND METHODS: Study received institutional approval, and written informed consent was obtained. Study was compliant with Health Insurance Portability and Accountability Act. Sextant biopsy, digital rectal examination, MR imaging, MR spectroscopy, and salvage radical prostatectomy with step-section pathologic examination were performed in nine patients with increasing prostate-specific antigen levels after external-beam radiation therapy. MR imaging criterion for tumor was a focal nodular region of reduced signal intensity at T2-weighted imaging. MR spectroscopic criteria for tumor were voxels with choline (Cho) plus creatine (Cr) to citrate (Cit) ratio ([Cho + Cr]/Cit) of at least 0.5 or voxels with detectable Cho and no Cit in the peripheral zone. Sensitiv...

OBJECTIVE: To design new models that combine clinical variables and biopsy data with magnetic resonance imaging (MRI) and MR spectroscopic imaging (MRSI) data, and assess their value in predicting the probability of insignificant prostate cancer. PATIENTS AND METHODS: In all, 220 patients (cT stage T1c or T2a, prostate‐specific antigen level <20 ng/mL, biopsy Gleason score 6) had MRI/MRSI before surgery and met the inclusion criteria for the study. The probability of insignificant cancer was recorded retrospectively and separately for MRI and combined MRI/MRSI on a 0–3 scale (0, definitely insignificant; − 3, definitely significant). Insignificant cancer was defined from surgical pathology as organ‐confined cancer of ≤ 0.5 cm ³ with no poorly differentiated elements. The accuracy of predicting insignificant prostate cancer was assessed using areas under receiver operating characteristic curves (AUCs), for previously reported clinical models and for newly generated MR models combining clinical variables, and biopsy data with MRI data (MRI model) and MRI/MRSI data (MRI/MRSI model). RESULTS: At pathology, 41% of patients had insignificant cancer; both MRI (AUC 0.803) and MRI/MRSI (AUC 0.854) models incorporating clinical, biopsy and MR data performed significantly better than the basic (AUC 0.574) and more comprehensive medium (AUC 0.726) clinical models. The P values for the differences between the models were: base vs medium model, <0.001; base vs MRI model, <0.001; base vs MRI/MRSI model, <0.001; medium vs MRI model, <0.018; medium vs MRI/MRSI model, <0.001. CONCLUSIONS: The new MRI and MRI/MRSI models performed better than the clinical models for predicting the probability of insignificant prostate cancer. After appropriate validation, the new MRI and MRI/MRSI models might help in counselling patients who are considering choosing deferred therapy.

PURPOSE: To determine whether chronic prostatitis affects three-dimensional proton magnetic resonance (MR) spectroscopic imaging in evaluation of disease in the peripheral zone. MATERIALS AND METHODS: Combined MR imaging and three-dimensional MR spectroscopic imaging data were examined retrospectively in 12 patients with radical prostatectomy specimens that contained regions of chronic prostatitis larger than 6 mm in the peripheral zone. The 6-mm restriction was based on MR spectroscopic imaging spatial resolution of 6.25 mm. Transverse T2-weighted MR images were reviewed for changes in signal intensity (SI): normal, suspicious for cancer (nodular focal low SI), or indeterminate (focal low SI that was not nodular or contour deforming or diffuse low SI). At MR spectroscopic imaging, proton spectra were considered suspicious for cancer if the ratio of choline plus creatine to citrate was more than 2 SDs above normal mean peripheral zone values. RESULTS: In the 12 patients, mean pretreatment prostate-specifi...

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.