Sunita Ghosh

BAIRDA: a novel in vitro setup to quantify radiobiological parameters for cervical cancer brachytherapy dose estimations

AbstractObjective. Brachytherapy (BT) dose prescriptions for locally advanced cervical cancer are made with account for the radiobiological parameters,α/βratio and halftime of repair (T1/2). However, a wide range of parameter values has been reported which can challenge commonly held equivalencies between dose prescriptions. This is the first reported study that aims to develop anin vitroexperimental technique using clinical high-dose-rate (HDR) and pulsed-dose-rate (PDR) Ir-192 brachytherapy afterloaders to quantify these parametersin vitroand to contextualize findings within contemporary practice.Approach. To efficiently quantifyα/βandT1/2,in vitroexperiments more reflective of clinical BT practice than traditional clonogenic survival assays were developed and applied to four squamous cell carcinoma cell lines (CaSki, C-33A, SiHa, and SW756). Radiation was delivered using single acute and fractionated dose treatments with a conventional irradiator and clinical HDR and PDR BT afterloaders. For the latter, a novelbrachytherapyafterloaderin vitroradiationdeliveryapparatus (BAIRDA) was developed.Main Results. Theα/βandT1/2values determined using BAIRDA and the conventional irradiator showed close agreement, validating the novel apparatus and technique. For CaSki, C-33A, SiHa, and SW756, the BAIRDA-measuredα/βratios (5.2 [4.6–5.8], 5.6 [4.5–6.6], 6.3 [4.9–7.7], and 5.3 [4.7–6.0] Gy, respectively) were consistently smaller, while theT1/2(3.3 [2.7–3.9], 2.7 [2.0–3.3], 2.8 (2.4–3.1], and 4.8 [4.1–5.4] hours) larger, than the widely accepted values in clinical practice (α/β= 10 Gy;T1/2 = 1.5 h).Significance.In vitroexperiments using BAIRDA provided evidence for differences between the conventionally selected and experimentally determinedα/βratio andT1/2. Treatment regimens using HDR-BT and PDR-BT, designed to deliver equivalent radiobiological doses based on conventional values, were shown to differ by up to 27 Gy EQD2 – an effect that could impact treatment outcomes in cervical cancer. Furthermore, with BAIRDA, we have developed a novel method for radiobiological research in BT.

Towards robust deep learning-based autosegmentation in MRI-planned gynecological brachytherapy: Importance of scalable development and comprehensive evaluation

To present comprehensive development and evaluation methodologies for a generalizable deep learning (DL)-driven autocontouring model of standard pelvic organs-at-risk (OARs) in MRI-planned cervical brachytherapy. A curated dataset of 200 3D-MRIs (85% training/validation, 15% testing) including multiple applicator types, varying treated anatomies, and manual contours of OARs (bladder, rectum, sigmoid, small bowel) by 3 physicians was utilized to develop an nnU-Net-based autocontouring model. Iterative tuning was conducted to determine the optimal hyperparameters and enhance evaluation metrics. Model performance was assessed using quantitative metrics, like geometric (e.g., Dice Coefficient (DC) and Hausdorff Distance 95th Percentile (HD95)) and dosimetric (dose-volume histograms (DVHs), dose differences (ΔD2cc)), and then correlated with qualitative physician-review (modified Turing and Likert tests). Geometric metrics were best for bladder (e.g., mean ± SD DC|HD95(mm) 0.93 ± 0.02|2.26 ± 1.07) with greater variability exhibited for small bowel (0.62 ± 0.16|24.90 ± 14.36). Dosimetric comparisons of manual vs predicted contours showed high agreement in DVHs, with mean ΔD2cc <0.60 Gy EQD2 The DL-based autocontouring model, trained on a heterogeneous in-house dataset, demonstrates clinical acceptability for OARs as determined by comprehensive evaluation. It also shows promise for translatability to target contouring, and adaptability to other gynecological (noncervix) brachytherapy applications. Differences in qualitative and quantitative results exist; directionality and magnitude should be considered in clinical usability assessments of brachytherapy autocontouring models.