A Novel Polymer-Encapsulated Multi-Imaging Modality Fiducial Marker with Positive Signal Contrast for Image-Guided Radiation Therapy.

BACKGROUND: Current fiducial markers (FMs) in external-beam radiotherapy (EBRT) for prostate cancer (PCa) cannot be positively visualized on magnetic resonance imaging (MRI) and create dose perturbation and significant imaging artifacts on computed tomography (CT) and MRI. We report our initial experience with clinical imaging of a novel multimodality FM, NOVA. METHODS: We tested Gold Anchor [G-FM], BiomarC [carbon, C-FM], and NOVA FMs in phantoms imaged with kilovoltage (kV) X-rays, transrectal ultrasound (TRUS), CT, and MRI. Artifacts of the FMs on CT were quantified by the relative streak artifacts level (rSAL) metric. Proton dose perturbations (PDPs) were measured with Gafchromic EBT3 film, with FMs oriented either perpendicular to or parallel with the beam axis. We also tested the performance of NOVA-FMs in a patient. RESULTS: NOVA-FMs were positively visualized on all 4 imaging modalities tested. The rSAL on CT was 0.750 ± 0.335 for 2-mm reconstructed slices. In F-tests, PDP was associated with marker type and depth of measurement (p < 10-6); at 5-mm depth, PDP was significantly greater for the G-FM (12.9%, p = 10-6) and C-FM (6.0%, p = 0.011) than NOVA (4.5%). EBRT planning with MRI/CT image co-registration and daily alignments using NOVA-FMs in a patient was feasible and reproducible. CONCLUSIONS: NOVA-FMs were positively visible and produced less PDP than G-FMs or C-FMs. NOVA-FMs facilitated MRI/CT fusion and identification of regions of interest.

Uncertainty in magnetic resonance imaging-based prostate postimplant dosimetry: Results of a 10-person human observer study, and comparisons with automatic postimplant dosimetry.

PURPOSE: Uncertainties in postimplant quality assessment (QA) for low-dose-rate prostate brachytherapy (LDRPBT) are introduced at two steps: seed localization and contouring. We quantified how interobserver variability (IoV) introduced in both steps impacts dose-volume-histogram (DVH) parameters for MRI-based LDRPBT, and compared it with automatically derived DVH parameters. METHODS AND MATERIALS: Twenty-five patients received MRI-based LDRPBT. Seven clinical observers contoured the prostate and four organs at risk, and 4 dosimetrists performed seed localization, on each MRI. Twenty-eight unique manual postimplant QAs were created for each patient from unique observer pairs. Reference QA and automatic QA were also performed for each patient. IoV of prostate, rectum, and external urinary sphincter (EUS) DVH parameters owing to seed localization and contouring was quantified with coefficients of variation. Automatically derived DVH parameters were compared with those of the reference plans. RESULTS: Coefficients of variation (CoVs) owing to contouring variability (CoVcontours) were significantly higher than those due to seed localization variability (CoVseeds) (median CoVcontours vs. median CoVseeds: prostate D90-15.12% vs. 0.65%, p < 0.001; prostate V100-5.36% vs. 0.37%, p < 0.001; rectum V100-79.23% vs. 8.69%, p < 0.001; EUS V200-107.74% vs. 21.18%, p < 0.001). CoVcontours were lower when the contouring observers were restricted to the 3 radiation oncologists, but were still markedly higher than CoVseeds. Median differences in prostate D90, prostate V100, rectum V100, and EUS V200 between automatically computed and reference dosimetry parameters were 3.16%, 1.63%, -0.00 mL, and -0.00 mL, respectively. CONCLUSIONS: Seed localization introduces substantially less variability in postimplant QA than does contouring for MRI-based LDRPBT. While automatic seed localization may potentially help improve workflow efficiency, it has limited potential for improving the consistency and quality of postimplant dosimetry.

Proton therapy for the management of localized prostate cancer: Long-term clinical outcomes at a comprehensive cancer center.

BACKGROUND AND PURPOSE: Proton therapy (PT) has emerged as a standard-of-care treatment option for localized prostate cancer at our comprehensive cancer center. However, there are few large-scale analyses examining the long-term clinical outcomes. Therefore, this article aims to evaluate the long-term effectiveness and toxicity of PT in patients with localized prostate cancer. MATERIALS AND METHODS: Review of 2772 patients treated from May 2006 through January 2020. Disease risk was stratified according to National Comprehensive Cancer Network guidelines as low [LR, n = 640]; favorable-intermediate [F-IR, n = 850]; unfavorable-intermediate [U-IR, n = 851]; high [HR, n = 315]; or very high [VHR, n = 116]. Biochemical failure and toxicity were analyzed using Kaplan-Meier estimates and multivariate models. RESULTS: The median patient age was 66 years; the median follow-up time was 7.0 years. Pelvic lymph node irradiation was prescribed to 28 patients (1%) (2 [0.2%] U-IR, 11 [3.5%] HR, and 15 [12.9%] VHR). The median dose was 78 Gy in 1.8-2.0 Gy(RBE) fractions. Freedom from biochemical relapse (FFBR) rates at 5 years and 10 years were 98.2% and 96.8% for the LR group; 98.3% and 93.6%, F-IR; 94.2% and 90.2%, U-IR; 94.3% and 85.2%, HR; and 86.1% and 68.5%, VHR. Two patients died of prostate cancer. Overall rates of late grade ≥ 3 GU and GI toxicity were 0.87% and 1.01%. CONCLUSIONS: Proton therapy for localized prostate cancer demonstrated excellent clinical outcomes in this large cohort, even among higher-risk groups with historically poor outcomes despite aggressive therapy.

Outcomes after PD-103 versus I-125 for low dose rate prostate brachytherapy monotherapy: An international, multi-institutional study.

BACKGROUND AND PURPOSE: Pd-103 and I-125 are commonly used in low dose rate (LDR) brachytherapy for prostate cancer. Comparisons of outcomes by isotope type are limited, but Pd-103 has distinct radiobiologic advantages over I-125 despite its lesser availability outside the United States. We evaluated oncologic outcomes after Pd-103 vs I-125 LDR monotherapy for prostate cancer. MATERIALS AND METHODS: We retrospectively analyzed databases at 8 institutions for men who received definitive LDR monotherapy with Pd-103 (n = 1,597) or I-125 (n = 7,504) for prostate cancer. Freedom from clinical failure (FFCF) and freedom from biochemical failure (FFBF) stratified by isotope were analyzed by Kaplan-Meier univariate and Cox multivariate analyses. Biochemical cure rates (prostate-specific antigen level ≤ 0.2 ng/mL between 3.5 and 4.5 years of follow-up) by isotype were calculated for men with at least 3.5 years of follow-up and compared by univariate and multivariate logistic regression. RESULTS: Compared with I-125, Pd-103 led to higher 7-year rates of FFBF (96.2% vs 87.6%, P < 0.001) and FFCF (96.5% vs 94.3%, P < 0.001). This difference held after multivariate adjustment for baseline factors (FFBF hazard ratio [HR] = 0.31, FFCF HR = 0.49, both P < 0.001). Pd-103 was also associated with higher cure rates on univariate (odds ratio [OR] = 5.9, P < 0.001) and multivariate (OR = 6.0, P < 0.001) analyses. Results retained significance in sensitivity analyses of data from the 4 institutions that used both isotopes (n = 2,971). CONCLUSIONS: Pd-103 monotherapy was associated with higher FFBF, FFCF, and biochemical cure rates, and suggests that Pd-103 LDR may lead to improved oncologic outcomes compared with I-125.

Automated Brain Metastases Segmentation With a Deep Dive Into False-positive Detection.

Purpose: The clinical management of brain metastases after stereotactic radiosurgery (SRS) is difficult, because a physician must review follow-up magnetic resonance imaging (MRI) scans to determine treatment outcome, which is often labor intensive. The purpose of this study was to develop an automated framework to contour brain metastases in MRI to help treatment planning for SRS and understand its limitations. Methods and Materials: Two self-adaptive nnU-Net models trained on postcontrast 3-dimensional T1-weighted MRI scans from patients who underwent SRS were analyzed. Performance was evaluated by computing positive predictive value (PPV), sensitivity, and Dice similarity coefficient (DSC). The training and testing sets included 3482 metastases on 845 patient MRI scans and 930 metastases on 206 patient MRI scans, respectively. Results: In the per-patient analysis, PPV was 90.1% ± 17.7%, sensitivity 88.4% ± 18.0%, DSC 82.2% ± 9.5%, and false positive (FP) 0.4 ± 1.0. For large metastases (≥6 mm), the per-patient PPV was 95.6% ± 17.5%, sensitivity 94.5% ± 18.1%, DSC 86.8% ± 7.5%, and FP 0.1 ± 0.4. The quality of autosegmented true-positive (TP) contours was also assessed by 2 physicians using a 5-point scale for clinical acceptability. Seventy-five percent of contours were assigned scores of 4 or 5, which shows that contours could be used as-is in clinical application, and the remaining 25% were assigned a score of 3, which means they needed minor editing only. Notably, a deep dive into FPs indicated that 9% were TP metastases not identified on the original radiology review, but identified on subsequent follow-up imaging (early detection). Fifty-four percent were real metastases (TP) that were identified but purposefully not contoured for target treatment, mainly because the patient underwent whole-brain radiation therapy before/after SRS treatment. Conclusions: These findings show that our tool can help radiologists and radiation oncologists detect and contour tumors from MRI, make precise decisions about suspicious lesions, and potentially find lesions at early stages.

Prospective Evaluation of Prostate and Organs at Risk Segmentation Software for MRI-based Prostate Radiation Therapy.

The segmentation of the prostate and surrounding organs at risk (OARs) is a necessary workflow step for performing dose-volume histogram analyses of prostate radiation therapy procedures. Low-dose-rate prostate brachytherapy (LDRPBT) is a curative prostate radiation therapy treatment that delivers a single fraction of radiation over a period of days. Prior studies have demonstrated the feasibility of fully convolutional networks to segment the prostate and surrounding OARs for LDRPBT dose-volume histogram analyses. However, performance evaluations have been limited to measures of global similarity between algorithm predictions and a reference. To date, the clinical use of automatic segmentation algorithms for LDRPBT has not been evaluated, to the authors' knowledge. The purpose of this work was to assess the performance of fully convolutional networks for prostate and OAR delineation on a prospectively identified cohort of patients who underwent LDRPBT by using clinically relevant metrics. Thirty patients underwent LDRPBT and were imaged with fully balanced steady-state free precession MRI after implantation. Custom automatic segmentation software was used to segment the prostate and four OARs. Dose-volume histogram analyses were performed by using both the original automatically generated contours and the physician-refined contours. Dosimetry parameters of the prostate, external urinary sphincter, and rectum were compared without and with the physician refinements. This study observed that physician refinements to the automatic contours did not significantly affect dosimetry parameters. Keywords: MRI, Neural Networks, Radiation Therapy, Radiation Therapy/Oncology, Genital/Reproductive, Prostate, Segmentation, Dosimetry Supplemental material is available for this article. © RSNA, 2022.

Computer-aided segmentation on MRI for prostate radiotherapy, part II: Comparing human and computer observer populations and the influence of annotator variability on algorithm variability.

BACKGROUND AND PURPOSE: Comparing deep learning (DL) algorithms to human interobserver variability, one of the largest sources of noise in human-performed annotations, is necessary to inform the clinical application, use, and quality assurance of DL for prostate radiotherapy. MATERIALS AND METHODS: One hundred fourteen DL algorithms were developed on 295 prostate MRIs to segment the prostate, external urinary sphincter (EUS), seminal vesicles (SV), rectum, and bladder. Fifty prostate MRIs of 25 patients undergoing MRI-based low-dose-rate prostate brachytherapy were acquired as an independent test set. Groups of DL algorithms were created based on the loss functions used to train them, and the spatial entropy (SE) of their predictions on the 50 test MRIs was computed. Five human observers contoured the 50 test MRIs, and SE maps of their contours were compared with those of the groups of the DL algorithms. Additionally, similarity metrics were computed between DL algorithm predictions and consensus annotations of the 5 human observers' contours of the 50 test MRIs. RESULTS: A DL algorithm yielded statistically significantly higher similarity metrics for the prostate than did the human observers (H) (prostate Matthew's correlation coefficient, DL vs. H: planning-0.931 vs. 0.903, p < 0.001; postimplant-0.925 vs. 0.892, p < 0.001); the same was true for the 4 organs at risk. The SE maps revealed that the DL algorithms and human annotators were most variable in similar anatomical regions: the prostate-EUS, prostate-SV, prostate-rectum, and prostate-bladder junctions. CONCLUSIONS: Annotation quality is an important consideration when developing, evaluating, and using DL algorithms clinically.

Computer-aided segmentation on MRI for prostate radiotherapy, Part I: Quantifying human interobserver variability of the prostate and organs at risk and its impact on radiation dosimetry.

BACKGROUND AND PURPOSE: Quantifying the interobserver variability (IoV) of prostate and periprostatic anatomy delineation on prostate MRI is necessary to inform its use for treatment planning, treatment delivery, and treatment quality assessment. MATERIALS AND METHODS: Twenty five prostate cancer patients underwent MRI-based low-dose-rate prostate brachytherapy (LDRPBT). The patients were scanned with a 3D T2-weighted sequence for treatment planning and a 3D T2/T1-weighted sequence for quality assessment. Seven observers involved with the LDRPBT workflow delineated the prostate, external urinary sphincter (EUS), seminal vesicles, rectum, and bladder on all 50 MRIs. IoV was assessed by measuring contour similarity metrics, differences in organ volumes, and differences in dosimetry parameters between unique observer pairs. Measurements from a group of 3 radiation oncologists (G1) were compared against those from a group consisting of the other 4 clinical observers (G2). RESULTS: IoV of the prostate was lower for G1 than G2 (Matthew's correlation coefficient [MCC], G1 vs. G2: planning-0.906 vs. 0.870, p < 0.001; postimplant-0.899 vs. 0.861, p < 0.001). IoV of the EUS was highest of all the organs for both groups, but was lower for G1 (MCC, G1 vs. G2: planning-0.659 vs. 0.402, p < 0.001; postimplant-0.684 vs. 0.398, p < 0.001). Large differences in prostate dosimetry parameters were observed (G1 maximum absolute prostate ΔD90: planning-76.223 Gy, postimplant-36.545 Gy; G1 maximum absolute prostate ΔV100: planning-13.927%, postimplant-8.860%). CONCLUSIONS: While MRI is optimal in the management of prostate cancer with radiation therapy, significant interobserver variability of the prostate and external urinary sphincter still exist.

Fully Balanced SSFP Without an Endorectal Coil for Postimplant QA of MRI-Assisted Radiosurgery (MARS) of Prostate Cancer: A Prospective Study.

PURPOSE: To investigate fully balanced steady-state free precession (bSSFP) with optimized acquisition protocols for magnetic resonance imaging (MRI)-based postimplant quality assessment of low-dose-rate (LDR) prostate brachytherapy without an endorectal coil (ERC). METHODS AND MATERIALS: Seventeen patients at a major academic cancer center who underwent MRI-assisted radiosurgery (MARS) LDR prostate cancer brachytherapy were imaged with moderate, high, or very high spatial resolution fully bSSFP MRIs without using an ERC. Between 1 and 3 signal averages (NEX) were acquired with acceleration factors (R) between 1 and 2, with the goal of keeping scan times between 4 and 6 minutes. Acquisitions with R >1 were reconstructed with parallel imaging and compressed sensing (PICS) algorithms. Radioactive seeds were identified by 3 medical dosimetrists. Additionally, some of the MRI techniques were implemented and tested at a community hospital; 3 patients underwent MARS LDR prostate brachytherapy and were imaged without an ERC. RESULTS: Increasing the in-plane spatial resolution mitigated partial volume artifacts and improved overall seed and seed marker visualization at the expense of reduced signal-to-noise ratio (SNR). The reduced SNR as a result of imaging at higher spatial resolution and without an ERC was partially compensated for by the multi-NEX acquisitions enabled by PICS. Resultant image quality was superior to the current clinical standard. All 3 dosimetrists achieved near-perfect precision and recall for seed identification in the 17 patients. The 3 postimplant MRIs acquired at the community hospital were sufficient to identify 208 out of 211 seeds implanted without reference to computed tomography (CT). CONCLUSIONS: Acquiring postimplant prostate brachytherapy MRI without an ERC has several advantages including better patient tolerance, lower costs, higher clinical throughput, and widespread access to precision LDR prostate brachytherapy. This prospective study confirms that the use of an ERC can be circumvented with fully bSSFP and advanced MRI scan techniques in a major academic cancer center and community hospital, potentially enabling postimplant assessment of MARS LDR prostate brachytherapy without CT.

Synthetic generation of DSC-MRI-derived relative CBV maps from DCE MRI of brain tumors.

PURPOSE: Perfusion MRI with gadolinium-based contrast agents is useful for diagnosis and treatment response evaluation of brain tumors. Dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are two gadolinium-based contrast agent perfusion imaging techniques that provide complementary information about the tumor vasculature. However, each requires a separate administration of a gadolinium-based contrast agent. The purpose of this retrospective study was to determine the feasibility of synthesizing relative cerebral blood volume (rCBV) maps, as computed from DSC MRI, from DCE MRI of brain tumors. METHODS: One hundred nine brain-tumor patients underwent both DCE and DSC MRI. Relative CBV maps were computed from the DSC MRI, and blood plasma volume fraction maps were computed from the DCE MRIs. Conditional generative adversarial networks were developed to synthesize rCBV maps from the DCE MRIs. Tumor-to-white matter ratios were calculated from real rCBV, synthetic rCBV, and plasma volume fraction maps and compared using correlation analysis. Real and synthetic rCBV in white and gray matter regions were also compared. RESULTS: Pearson correlation analysis showed that both the tumor rCBV and tumor-to-white matter ratios in the synthetic and real rCBV maps were strongly correlated (ρ = 0.87, P < .05 and ρ = 0.86, P < .05, respectively). Tumor plasma volume fraction and real rCBV were not strongly correlated (ρ = 0.47). Bland-Altman analysis showed a mean difference between the synthetic and real rCBV tumor-to-white matter ratios of 0.20 with a 95% confidence interval of ±0.47. CONCLUSION: Realistic rCBV maps can be synthesized from DCE MRI and contain quantitative information, enabling robust brain-tumor perfusion imaging of DSC and DCE parameters with a single gadolinium-based contrast agent administration.

The American Brachytherapy Society prostate brachytherapy LDR/HDR simulation workshops: Hands-on, step-by-step training in the process of quality assurance.

PURPOSE: Education and training on prostate brachytherapy for radiation oncology and medical physics residents in the United States is inadequate, resulting in fewer competent radiation oncology personnel to perform implants, and is a factor in the subsequent decline of an important, potentially curative cancer treatment modality for patients with cancer. The American Brachytherapy Society (ABS) leadership has recognized the need to establish a sustainable medical simulation low-dose-rate (LDR) and high-dose-rate (HDR) brachytherapy workshop program that includes physician-physicist teams to rapidly translate knowledge to establish high-quality brachytherapy programs. METHODS: The ABS, in partnership with industry and academia, has held three radiation oncology team-based LDR/HDR workshops composed of physician-physicist teams in Chicago in 2017, in Houston in 2018, and in Denver in 2019. The predefined key metric of success is the number of attendees who returned to their respective institutions and were actively performing brachytherapy within 6 months of the prostate brachytherapy workshop. RESULTS: Of the 111 physician/physicist teams participating in the Chicago, Houston, and Denver prostate brachytherapy workshops, 87 (78%) were actively performing prostate brachytherapy (51 [59%] HDR and 65 [75%] LDR). CONCLUSIONS: The ABS prostate brachytherapy LDR/HDR simulation workshop has provided a successful education and training structure for medical simulation of the critical procedural steps in quality assurance to shorten the learning curve for delivering consistently high-quality brachytherapy implants for patients with prostate cancer. An ABS initiative, intended to bend the negative slope of the brachytherapy curve, is currently underway to train 300 new competent brachytherapy teams over the next 10 years.

Development, implementation, and outcomes of a simulation-based medical education (SBME) prostate brachytherapy workshop for radiation oncology residents.

PURPOSE: Despite a preponderance of data demonstrating strong clinical outcomes and cost-effectiveness, prostate brachytherapy use and competency continue to decline. Enhanced resident education may help reverse this trend. We therefore developed and implemented a simulation-based medical education course for low-dose-rate prostate brachytherapy (LDR-PB). MATERIALS AND METHODS: A 1-week LDR-PB course comprised four 1-h lectures on clinical outcomes, physics, radiobiology, and anatomy/contouring, followed by a 4.5-h simulation session on ultrasound-guided prostate phantom implantation, was developed for radiation oncology residents at an academic institution. A 10-statement Likert-scale survey and 20-question multiple-choice test were administered 1 week before and 4 weeks after the course. RESULTS: Precourse and postcourse instruments were completed by 24 and 20 residents, respectively. The median number of prior LDR-PB cases after at least one genitourinary rotation was 10.5 (range 5-20). Overall mean test scores were significantly improved (55% before the course vs 68% after the course; p = 0.010). Mean Likert scores significantly increased on nine of 10 survey statements and were significantly increased overall (2.4 before the course vs 3.3 after the course, p < 0.001). When asked about interest in performing brachytherapy after residency, 37.5% of residents "agreed" or "strongly agreed" before the course vs 50% after the course (p = 0.41). Those with higher postresidency brachytherapy interest (scores of 4-5 vs 1-3) had significantly more LDR-PB cases (11.2 vs 5.3 cases; p = 0.005). CONCLUSIONS: A 1-week simulation-based medical education course for LDR-PB can improve didactic performance and self-reported technical competence/confidence, and may increase overall enthusiasm for brachytherapy. Future studies at our institution will incorporate evaluation of implant quality and assessment of procedural competence into this framework. Residency programs should dedicate resources to this essential component of radiation oncology.

MetNet: Computer-aided segmentation of brain metastases in post-contrast T1-weighted magnetic resonance imaging.

PURPOSE: Brain metastases are manually contoured during stereotactic radiosurgery (SRS) treatment planning, which is time-consuming, potentially challenging, and laborious. The purpose of this study was to develop and investigate a 2-stage deep learning (DL) approach (MetNet) for brain metastasis segmentation in pre-treatment magnetic resonance imaging (MRI). MATERIALS AND METHODS: We retrospectively analyzed postcontrast 3D T1-weighted spoiled gradient echo MRIs from 934 patients who underwent SRS between August 2009 and August 2018. Neuroradiologists manually identified brain metastases in the MRIs. The treating radiation oncologist or physicist contoured the brain metastases. We constructed a 2-stage DL ensemble consisting of detection and segmentation models to segment the brain metastases on the MRIs. We evaluated the performance of MetNet by computing sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC) with respect to metastasis size, as well as free-response receiver operating characteristics. RESULTS: The 934 patients (mean [±standard deviation] age 59 ± 13 years, 474 women) were randomly split into 80% training and 20% testing groups (748:186). For patients with metastases 1-52 mm (n = 766), 648 (85%) were detected and segmented with a mean segmentation DSC of 81% ± 15%. Patient-averaged sensitivity was 88% ± 19%, PPV was 58% ± 25%, and DSC was 85% ± 13% with 3 ± 3 false positives (FPs) per patient. When considering only metastases ≥6 mm, patient-averaged sensitivity was 99% ± 5%, PPV was 67% ± 28%, and DSC was 87% ± 13% with 1 ± 2 FPs per patient. CONCLUSION: MetNet can segment brain metastases across a broad range of metastasis sizes with high sensitivity, low FPs, and high segmentation accuracy in postcontrast T1-weighted MRI, potentially aiding treatment planning for SRS.

Machine Segmentation of Pelvic Anatomy in MRI-Assisted Radiosurgery (MARS) for Prostate Cancer Brachytherapy.

PURPOSE: To investigate machine segmentation of pelvic anatomy in magnetic resonance imaging (MRI)-assisted radiosurgery (MARS) for prostate cancer using prostate brachytherapy MRIs acquired with different pulse sequences and image contrasts. METHODS AND MATERIALS: Two hundred 3-dimensional (3D) preimplant and postimplant prostate brachytherapy MRI scans were acquired with a T2-weighted sequence, a T2/T1-weighted sequence, or a T1-weighted sequence. One hundred twenty deep machine learning models were trained to segment the prostate, seminal vesicles, external urinary sphincter, rectum, and bladder using the MRI scans acquired with T2-weighted and T2/T1-weighted image contrast. The deep machine learning models consisted of 18 fully convolutional networks (FCNs) with different convolutional encoders. Both 2-dimensional and 3D U-Net FCNs were constructed for comparison. Six objective functions were investigated: cross-entropy, Jaccard distance, focal loss, and 3 variations of Tversky distance. The performance of the models was compared using similarity metrics, including pixel accuracy, Jaccard index, Dice similarity coefficient (DSC), 95% Hausdorff distance, relative volume difference, Matthews correlation coefficient, precision, recall, and average symmetrical surface distance. We selected the highest-performing architecture and investigated how the amount of training data, use of skip connections, and data augmentation affected segmentation performance. In addition, we investigated whether segmentation on T1-weighted MRI was possible with FCNs trained on only T2-weighted and T2/T1-weighted image contrast. RESULTS: Overall, an FCN with a DenseNet201 encoder trained via cross-entropy minimization yielded the highest combined segmentation performance. For the 53 3D test MRI scans acquired with T2-weighted or T2/T1-weighted image contrast, the DSCs of the prostate, external urinary sphincter, seminal vesicles, rectum, and bladder were 0.90 ± 0.04, 0.70 ± 0.15, 0.80 ± 0.12, 0.91 ± 0.06, and 0.96 ± 0.04, respectively, after model fine-tuning. For the 5 T1-weighted images, the DSCs of these organs were 0.82 ± 0.07, 0.17 ± 0.15, 0.46 ± 0.21, 0.87 ± 0.06, and 0.88 ± 0.05, respectively. CONCLUSIONS: Machine segmentation of the prostate and surrounding anatomy on 3D MRIs acquired with different pulse sequences for MARS low-dose-rate prostate brachytherapy is possible with a single FCN.

A biochemical definition of cure after brachytherapy for prostate cancer.

BACKGROUND AND PURPOSE: To identify a PSA threshold value at an intermediate follow-up time after low dose rate (LDR) prostate brachytherapy associated with cure, defined as long-term (10-15 year) freedom from prostate cancer. MATERIALS AND METHODS: Data from 7 institutions for 14,220 patients with localized prostate cancer treated with LDR brachytherapy, either alone (8552) or with external beam radiotherapy (n = 1175), androgen deprivation (n = 3165), or both (n = 1328), were analyzed. Risk distribution was 42.4% favorable, 49.2% intermediate, and 8.4% high-risk. Patients with clinical failure before 3.5 years were excluded. Kaplan-Meier analysis was used with clinical failure (local, distant, regional or biochemical triggering salvage) as an endpoint for each of four PSA categories: PSA ≤ 0.2, >0.2 to ≤0.5, >0.5 to ≤1.0, and >1.0 ng/mL. PSA levels at 4 years (±6 months) in 8746 patients without clinical failure were correlated with disease status at 10-15 years. RESULTS: For the 77.1% of patients with 4-year PSA ≤ 0.2, the freedom-from-recurrence (FFR) rates were 98.7% (95% CI 98.3-99.0) at 10 years and 96.1% (95% CI 94.8-97.2) at 15 years. Three independent validation cohorts confirmed 97-99% 10-year FFR rates with 4-year PSA ≤ 0.2. Successive PSA categories were associated with diminished disease-free rates at 10 and 15 years. PSA category was strongly associated with treatment success (p < 0.0005). CONCLUSIONS: Since 98.7% of patients with PSA ≤ 0.2 ng/mL at 4 years after LDR prostate brachytherapy were disease-free beyond 10 years, we suggest adopting this biochemical definition of cure for patients with ≥4 years' follow-up after LDR brachytherapy.

Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors.

Background Brain metastases are manually identified during stereotactic radiosurgery (SRS) treatment planning, which is time consuming and potentially challenging. Purpose To develop and investigate deep learning (DL) methods for detecting brain metastasis with MRI to aid in treatment planning for SRS. Materials and Methods In this retrospective study, contrast material-enhanced three-dimensional T1-weighted gradient-echo MRI scans from patients who underwent gamma knife SRS from January 2011 to August 2018 were analyzed. Brain metastases were manually identified and contoured by neuroradiologists and treating radiation oncologists. DL single-shot detector (SSD) algorithms were constructed and trained to map axial MRI slices to a set of bounding box predictions encompassing metastases and associated detection confidences. Performances of different DL SSDs were compared for per-lesion metastasis-based detection sensitivity and positive predictive value (PPV) at a 50% confidence threshold. For the highest-performing model, detection performance was analyzed by using free-response receiver operating characteristic analysis. Results Two hundred sixty-six patients (mean age, 60 years ± 14 [standard deviation]; 148 women) were randomly split into 80% training and 20% testing groups (212 and 54 patients, respectively). For the testing group, sensitivity of the highest-performing (baseline) SSD was 81% (95% confidence interval [CI]: 80%, 82%; 190 of 234) and PPV was 36% (95% CI: 35%, 37%; 190 of 530). For metastases measuring at least 6 mm, sensitivity was 98% (95% CI: 97%, 99%; 130 of 132) and PPV was 36% (95% CI: 35%, 37%; 130 of 366). Other models (SSD with a ResNet50 backbone, SSD with focal loss, and RetinaNet) yielded lower sensitivities of 73% (95% CI: 72%, 74%; 171 of 234), 77% (95% CI: 76%, 78%; 180 of 234), and 79% (95% CI: 77%, 81%; 184 of 234), respectively, and lower PPVs of 29% (95% CI: 28%, 30%; 171 of 581), 26% (95% CI: 26%, 26%; 180 of 681), and 13% (95% CI: 12%, 14%; 184 of 1412). Conclusion Deep-learning single-shot detector models detected nearly all brain metastases that were 6 mm or larger with limited false-positive findings using postcontrast T1-weighted MRI. © RSNA, 2020 See also the editorial by Kikinis and Wells in this issue.

Development and clinical implementation of SeedNet: A sliding-window convolutional neural network for radioactive seed identification in MRI-assisted radiosurgery (MARS).

PURPOSE: To develop and evaluate a sliding-window convolutional neural network (CNN) for radioactive seed identification in MRI of the prostate after permanent implant brachytherapy. METHODS: Sixty-eight patients underwent prostate cancer low-dose-rate (LDR) brachytherapy using radioactive seeds stranded with positive contrast MR-signal seed markers and were scanned using a balanced steady-state free precession pulse sequence with and without an endorectal coil (ERC). A sliding-window CNN algorithm (SeedNet) was developed to scan the prostate images using 3D sub-windows and to identify the implanted radioactive seeds. The algorithm was trained on sub-windows extracted from 18 patient images. Seed detection performance was evaluated by computing precision, recall, F1 -score, false discovery rate, and false-negative rate. Seed localization performance was evaluated by computing the RMS error (RMSE) between the manually identified and algorithm-inferred seed locations. SeedNet was implemented into a clinical software package and evaluated on sub-windows extracted from 40 test patients. RESULTS: SeedNet achieved 97.6 ± 2.2% recall and 97.2 ± 1.9% precision for radioactive seed detection and 0.19 ± 0.04 mm RMSE for seed localization in the images acquired with an ERC. Without the ERC, the recall remained high, but the false-positive rate increased; the RMSE of the seed locations increased marginally. The clinical integration of SeedNet slightly increased the run-time, but the overall run-time was still low. CONCLUSION: SeedNet can be used to perform automated radioactive seed identification in prostate MRI after LDR brachytherapy. Image quality improvement through pulse sequence optimization is expected to improve SeedNet's performance when imaging without an ERC.

Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging.

Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Several example applications using clinical images were developed and tested to demonstrate the capabilities of DLAE. Additionally, a model deployment example was demonstrated in which DLAE was used to integrate two trained models into a commercial clinical software package.

Parallel imaging compressed sensing for accelerated imaging and improved signal-to-noise ratio in MRI-based postimplant dosimetry of prostate brachytherapy.

PURPOSE: To investigate the feasibility of using parallel imaging compressed sensing (PICS) to reduce scan time and improve signal-to-noise ratio (SNR) in MRI-based postimplant dosimetry of prostate brachytherapy. METHODS AND MATERIALS: Ten patients underwent low-dose-rate prostate brachytherapy with radioactive seeds stranded with positive magnetic resonance-signal seed markers and were scanned on a Siemens 1.5T Aera. MRI comprised a fully balanced steady-state free precession sequence with two 18-channel external pelvic array coils with and without a rigid two-channel endorectal coil. The fully sampled data sets were retrospectively subsampled with increasing acceleration factors and reconstructed with parallel imaging and compressed sensing algorithms. The images were assessed in a blinded reader study by board-certified care providers. Rating scores were compared for statistically significant differences between reconstruction types. RESULTS: Images reconstructed from subsampling up to an acceleration factor of 4 with PICS demonstrated consistently sufficient quality for dosimetry with no apparent loss of SNR, anatomy depiction, or seed/marker conspicuity when compared to the fully sampled images. Images obtained with acceleration factors of 5 or 6 revealed reduced spatial resolution and seed marker contrast. Nevertheless, the reader study revealed that images obtained with an acceleration factor of up to 5 and reconstructed with PICS were adequate-to-good for postimplant dosimetry. CONCLUSIONS: Combined parallel imaging and compressed sensing can substantially reduce scan time in fully balanced steady-state free precession imaging of the prostate while maintaining adequate-to-good image quality for postimplant dosimetry. The saved scan time can be used for multiple signal averages and improved SNR, potentially obviating the need for an endorectal coil in MRI-based postimplant dosimetry.