Background: Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four‐compartment restriction spectrum imaging (RSI₄) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI₄‐C₁, yielded greatest tumor conspicuity.Purpose: To evaluate the slowest diffusion compartment of a four‐compartment spectrum imaging model (RSI₄‐C₁) as a quantitative voxel‐level classifier of PCa.Study Type: Retrospective.Subjects: Forty‐six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty‐three men had benign prostates, and the other 23 men had PCa.Field Strength/Sequence: A 3 T, multishell diffusion‐weighted and axial T2‐weighted sequences.Assessment: High‐confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI₄‐C₁ and conventional ADC. Classifier images were also generated.Statistical Tests: Voxel‐level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient‐level case resampling. RSI₄‐C₁ was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false‐positive rate for a sensitivity of 90% (FPR₉₀). Statistical significance was assessed using bootstrap difference with two‐sided α = 0.05.Results: RSI₄‐C₁ outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951–0.991] vs. 0.922 [0.878–0.948]) and lower FPR₉₀ (0.032 [0.009–0.082] vs. 0.201 [0.132–0.290]). These improvements were statistically significant (P < 0.05).Data Conclusion: RSI₄‐C₁ yielded a quantitative, voxel‐level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false‐positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning.Evidence Level: 3Technical Efficacy: Stage 2

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

Invasive lobular carcinomas have an atypical clinical manifestation and imaging appearance, and mammographic, US, and MRI findings can help radiologists accurately assess tumor size and evaluate fo...

Purpose: Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model. Experimental Design: Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C1 and C2 and their product, C1C2, and signal fractions F1, F2, and F1F2 were compared with the image defined on maximum b-value (DWImax), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (Kapp). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC. Results: Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008–0.024] for C1C2, 0.136 (95% CI, 0.092–0.180) for C1, 0.068 (95% CI, 0.049–0.087) for C2, 0.462 (95% CI, 0.425–0.499) for F1F2, 0.832 (95% CI, 0.797–0.868) for F1, 0.176 (95% CI, 0.150–0.203) for F2, 0.159 (95% CI, 0.114–0.204) for DWImax, 0.731 (95% CI, 0.692–0.770) for ADC, and 0.684 (95% CI, 0.660–0.709) for Kapp. Mean ROC AUC for C1C2 was 0.984 (95% CI, 0.977–0.991). Conclusions: The C1C2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.

BI-RADS category 3 is used for findings with a low malignancy probability and warrants careful short-interval follow-up imaging, and misconceptions about category 3 and new data supporting its use in breast MRI highlight the need for continued education on appropriate use in multimodality breast imaging.