Michael Roumeliotis

The American Brachytherapy Society (ABS) consensus guidance for hybrid intracavitary interstitial brachytherapy for locally advanced cervical cancer

The purpose of this consensus statement from the American Brachytherapy Society (ABS) is to summarize important considerations for adding interstitial needles to intracavitary implants for cervix cancer brachytherapy. A panel of experts in Gynecologic Brachytherapy, including both physicians and physicists completed surveys and met virtually on multiple occasions to discuss and define current practices and approaches in order to summarize these for the ABS community. This document was drafted, reviewed and approved by the full panel and the ABS Board of Directors. Hybrid brachytherapy is increasingly being utilized in cervix brachytherapy. In incorporating supplementary interstitial needles there are numerous key considerations including resources for an emergency, planning considerations, applicator selection, and pre-, intra- and postprocedural imaging. Additionally, approaches to challenging cases that would often benefit from a hybrid approach are discussed. While hybrid brachytherapy offers dosimetric advantages for cervix brachytherapy, it increases procedure complexity. Being aware of the necessary resources and defining considerations can help mitigate some of the challenges and improve procedural success.

Dual convolution-transformer UNet (DCT-UNet) for organs at risk and clinical target volume segmentation in MRI for cervical cancer brachytherapy

Abstract Objective. MRI is the standard imaging modality for high-dose-rate brachytherapy of cervical cancer. Precise contouring of organs at risk (OARs) and high-risk clinical target volume (HR-CTV) from MRI is a crucial step for radiotherapy planning and treatment. However, conventional manual contouring has limitations in terms of accuracy as well as procedural time. To overcome these, we propose a deep learning approach to automatically segment OARs (bladder, rectum, and sigmoid colon) and HR-CTV from female pelvic MRI. Approach. In the proposed pipeline, a coarse multi-organ segmentation model first segments all structures, from which a region of interest is computed for each structure. Then, each organ is segmented using an organ-specific fine segmentation model separately trained for each organ. To account for variable sizes of HR-CTV, a size-adaptive multi-model approach was employed. For coarse and fine segmentations, we designed a dual convolution-transformer UNet (DCT-UNet) which uses dual-path encoder consisting of convolution and transformer blocks. To evaluate our model, OAR segmentations were compared to the clinical contours drawn by the attending radiation oncologist. For HR-CTV, four sets of contours (clinical + three additional sets) were obtained to produce a consensus ground truth as well as for inter/intra-observer variability analysis. Main results. DCT-UNet achieved dice similarity coefficient (mean ± SD) of 0.932 ± 0.032 (bladder), 0.786 ± 0.090 (rectum), 0.663 ± 0.180 (sigmoid colon), and 0.741 ± 0.076 (HR-CTV), outperforming other state-of-the-art models. Notably, the size-adaptive multi-model significantly improved HR-CTV segmentation compared to a single-model. Furthermore, significant inter/intra-observer variability was observed, and our model showed comparable performance to all observers. Computation time for the entire pipeline per subject was 12.59 ± 0.79 s, which is significantly shorter than the typical manual contouring time of >15 min. Significance. These experimental results demonstrate that our model has great utility in cervical cancer brachytherapy by enabling fast and accurate automatic segmentation, and has potential in improving consistency in contouring. DCT-UNet source code is available at https://github.com/JHU-MICA/DCT-UNet.

Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapy

To Demonstrate the clinical validation of a machine learning (ML) model for applicator and interstitial needle prediction in gynecologic brachytherapy through a prospective clinical study in a single institution. The study included cervical cancer patients receiving high-dose-rate brachytherapy using intracavitary (IC) or hybrid interstitial (IC/IS) applicators. For each patient, the primary radiation oncologist contoured the high-risk clinical target volume on a pre-brachytherapy MRI, indicated the approximate applicator location, and made a clinical determination of the first fraction applicator. A pre-trained ML model predicted the applicator and IC/IS needle arrangement using tumor geometry. Following the first fraction, ML and radiation oncologist predictions were compared and a replanning study determined the applicator providing optimal organ-at-risk (OAR) dosimetry. The ML-predicted applicator and needle arrangement and the clinical determination were compared to this dosimetric ground truth. Ten patients were accrued from December 2020 to October 2022. Compared to the dosimetrically optimal applicator, both the radiation oncologist and ML had an accuracy of 70%. ML demonstrated better identification of patients requiring IC/IS applicators and provided balanced IC and IC/IS predictions. The needle selection model achieved an average accuracy of 82.5%. ML-predicted needle arrangements matched or improved plan quality when compared to clinically selected arrangements. Overall, ML predictions led to an average total improvement of 2.0 Gy to OAR doses over three treatment fractions when compared to clinical predictions. In the context of a single institution study, the presented ML model demonstrates valuable decision-support for the applicator and needle selection process with the potential to provide improved dosimetry. Future work will include a multi-center study to assess generalizability.

Primary vaginal cancer treated with high-dose rate brachytherapy and intraprocedural magnetic resonance imaging

To report outcomes among primary vaginal cancer patients treated definitively with either external beam radiation therapy plus high-dose rate (HDR) brachytherapy (EBRT-BT) or BT (BT) alone with placement of interstitial catheters under magnetic resonance imaging (MRI) guidance. Retrospective review of 41 patients treated for primary vaginal cancer from 2016 to 2022. Kaplan-Meier (KM) estimates were generated for disease-free survival (DFS), local control (LC), and overall survival (OS). Median follow-up was 28 months (range 2-82 months). A total of 36 patients had EBRT-BT, 5 had BT alone. Forty patients had template interstitial and 1 had a multichannel cylinder. Among patients who received EBRT-BT, median total D90 EQD2 was 68.3 Gray (Gy) (range 56.6-91.5 Gy); BT only, median 40.3 Gy (range 38.1-86.3 Gy). No patient experienced local only failure. Relapse occurred in 12 patients treated with EBRT-BT versus 1 with BT alone group. For the EBRT-BT cohort versus BT only cohort, 2-year OS was 81% versus 60%, DFS 61% versus 40%, and LC was 94% versus 80%, respectively. For the entire cohort, 2-year OS was 67%, and median OS was 5 years. The 2-year DFS was 57% and 2-year LC was 93%. The most common any grade acute treatment-related toxicity were grade 1 vaginal pain and stenosis. Grade 3 acute and late toxicities were minimal. MRI-guided HDR BT for primary vaginal cancer yields high rates of local control with limited toxicities. Lower rates of distant control indicate the need for newer options such as immunotherapy or other systemic agents.

Automatic digitization of applicator and catheters for MRI-guided cervical cancer brachytherapy

MRI is the standard imaging modality for contouring organs-at-risk and clinical target volume in cervical cancer brachytherapy, and is widely used along with CT for treatment planning and image guidance. However, MRI-CT fusion-based approach is time-consuming and error-prone as it requires two imaging sessions and image registration. To realize more efficient and streamlined MRI-guided workflow, we propose an automatic method for digitizing the applicator and catheters using MRI alone. Applicator digitization consists of applicator mesh reconstruction, applicator ring identification, and alignment of the mesh model with MRIs. For catheter digitization, we employ an uncertainty-aware deep-learning model that simultaneously segments catheters and computes uncertainty on its prediction. These uncertainty facilitate initial localization of the catheters and subsequent refinement. This study was performed on 35 T2-weighted MRIs from 30 cervical cancer patients treated with the Venezia applicator. The dataset was divided into 80% for development and 20% for testing. The method successfully digitized all applicators, with mean translation and rotation errors of 1.13 ± 0.26 mm and 2.19 ± 2.09°, respectively. All catheters except one were successfully digitized with shaft and tip errors of 0.74 ± 0.32 mm and 2.52 ± 2.04 mm, respectively. Furthermore, plans derived from the automatic digitization showed no significant differences compared to clinical plans (p > 0.05). The proposed MRI-based applicator and catheters digitization simplifies the brachytherapy planning process by eliminating the need for CT and manual tasks. Our results demonstrate that this approach is feasible and can be integrated into clinical workflows, offering potential improvements in efficiency and accuracy.