Christine H. Feng

Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10^-3, 2.8 × 10^-3, and >3.0 ×10^-2 mm²/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.

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

Purpose: Design and evaluate a workflow using commercially available artificial intelligence tools for automated hippocampal segmentation and treatment planning to efficiently generate clinically acceptable hippocampal-avoidant whole brain (HA-WBRT) radiotherapy plans. Methods and Materials: We retrospectively identified 100 consecutive adult patients treated for brain metastases outside the hippocampal region. Each patient’s T1 post-contrast brain MRI was processed using FDA-approved software that provides segmentations of brain structures in 5-7 minutes. Automated hippocampal segmentations were reviewed for accuracy and edited manually if necessary, then converted to files compatible with a commercial treatment planning system, where hippocampal avoidance regions and planning target volumes (PTV) were generated. Other organs-at-risk (OARs) were previously contoured per clinical routine. A RapidPlan knowledge-based planning routine was applied for a prescription of 30 Gy in 10 fractions using volumetric modulated arc therapy (VMAT) delivery. Plans were evaluated based on NRG CC001 dose-volume objectives. Results: Of the 100 cases, 99 (99%) had acceptable automated hippocampi segmentations without manual intervention. Knowledge-based planning was applied to all cases; the median processing time was 9 minutes 59 seconds (range 6:53 – 13:31). All plans met per-protocol dose-volume objectives for PTV per the NRG CC001 protocol. For comparison, only 66.0% of plans on NRG CC001 met PTV goals per protocol, with 26.3% within acceptable variation. In this study, 43 plans (43%) met OAR constraints, and the remaining 57 (57%) were within acceptable variation, compared to 42.9% and 48.6% on NRG CC001, respectively. No plans in this study had unacceptable dose to OARs, compared to 0.8% of manually generated plans from NRG CC001. Conclusion: An automated pipeline harnessing the efficiency of commercially available artificial intelligence tools can generate clinically acceptable VMAT HA-WBRT plans with minimal manual intervention. This process could improve clinical efficiency for a treatment established to improve patient outcomes over standard WBRT.

Background: Optimal prostate cancer (PCa) screening strategies will focus on men likely to have potentially lethal disease. Age‐specific incidence rates (ASIRs) by modern clinical risk groups could inform risk stratification efforts for screening. Methods: This cross‐sectional population study identified all men diagnosed with PCa in Norway from 2014 to 2017 (n = 20,356). Age, Gleason score (primary plus secondary), and clinical stage were extracted. Patients were assigned to clinical risk groups: low, favorable intermediate, unfavorable intermediate, high, regional, and metastatic. Chi‐square tests analyzed the independence of Gleason scores and modern PCa risk groups with age. ASIRs for each risk group were calculated as the product of Norwegian ASIRs for all PCa and the proportions observed for each risk category. Results: Older age was significantly associated with a higher Gleason score and more advanced disease. The percentages of men with Gleason 8 to 10 disease among men aged 55 to 59, 65 to 69, 75 to 79, and 85 to 89 years were 16.5%, 23.4%, 37.2%, and 59.9%, respectively ( P < .001); the percentages of men in the same age groups with at least high‐risk disease were 29.3%, 39.1%, 60.4%, and 90.6%, respectively ( P < .001). The maximum ASIRs (per 100,000 men) for low‐risk, favorable intermediate‐risk, unfavorable intermediate‐risk, high‐risk, regional, and metastatic disease were 157.1 for those aged 65 to 69 years, 183.8 for those aged 65 to 69 years, 194.8 for those aged 70 to 74 years, 408.3 for those aged 75 to 79 years, 159.7 for those aged ≥85 years, and 314.0 for those aged ≥85 years, respectively. At the ages of 75 to 79 years, the ASIR of high‐risk disease was approximately 6 times greater than the ASIR at 55 to 59 years. Conclusions: The risk of clinically significant localized PCa increases with age. Healthy older men may benefit from screening.

This study was designed to estimate radiation‐induced secondary cancer risks from high‐dose‐rate (HDR) brachytherapy and external radiotherapy for patients with cervical cancer based on measurements of doses absorbed by various organs. Organ doses from HDR brachytherapy and external radiotherapy were measured using glass rod dosimeters. Doses to out‐of‐field organs were measured at various locations inside an anthropomorphic phantom. Brachytherapy‐associated organ doses were measured using a specialized phantom that enabled applicator insertion, with the pelvis portion of the existing anthropomorphic phantom replaced by this new phantom. Measured organ doses were used to calculate secondary cancer risk based on Biological Effects of Ionizing Radiation (BEIR) VII models. In both treatment modalities, organ doses per prescribed dose (PD) mostly depended on the distance between organs. The locations showing the highest and lowest doses were the right kidney (external radiotherapy: 215.2 mGy; brachytherapy: 655.17 mGy) and the brain (external radiotherapy: 15.82 mGy; brachytherapy: 2.49 mGy), respectively. Organ doses to nearby regions were higher for brachytherapy than for external beam therapy, whereas organ doses to distant regions were higher for external beam therapy. Organ doses to distant treatment regions in external radiotherapy were due primarily to out‐of‐field radiation resulting from scattering and leakage in the gantry head. For brachytherapy, the highest estimated lifetime attributable risk per 100,000 population was to the stomach (88.6), whereas the lowest risks were to the brain (0.4) and eye (0.4); for external radiotherapy, the highest and lowest risks were to the thyroid (305.1) and brain (2.4). These results may help provide a database on the impact of radiotherapy‐induced secondary cancer incidence during cervical cancer treatment, as well as suggest further research on strategies to counteract the risks of radiotherapy‐associated secondary malignancies.PACS number(s): 87.52.‐g, 87.52.Px, 87.53.Dq, 87.53.Jw