The dosimetric impact of vaginal balloon-packing on intracavitary high-dose-rate brachytherapy for gynecological cancer

Purpose: We perform a clinical retrospective study to determine whether a vaginal balloon-packing system provides a dosimetric reduction to organs at risk (OARs) versus traditional gauze packing for gynecological high-dose-rate brachytherapy (HDR-BT). We also test various balloon filling materials for optimizing imaging quality. Material and methods: Filling materials for balloon-packing were evaluated based on imaging quality with X-ray, computerized tomography, and magnetic resonance imaging modalities. We then retrospectively reviewed 45 HDR-BT plans of 18 patients performed with gauze packing and 39 plans of 16 patients performed with balloon-packing. Twelve patients received both gauze and balloon-packing. HDR-BT was delivered with an iridium-192 afterloader and a Fletcher-Suit-Declos-style T&O applicator. At each fraction, 3D imaging was obtained. The D2cc values of OARs were calculated, as well as ICRU-defined point doses. Results: In the 84 HDR fractions reviewed, vaginal balloon-packing provides statistically equivalent doses to rectum, bladder, and sigmoid compared to gauze packing. On average balloon-packing produced average reductions of 3.3% and 6.9% in the rectal and sigmoid D2ccdoses and an increase of 3.2% to the bladder D2cc dose (normalized to prescription dose), although none of these values were statistically significant for the twelve patients who received both gauze and balloon-packing (32 and 40 total fractions, respectively). Conclusions: In the 84 HDR fractions analyzed, vaginal balloon-packing is as effective as gauze packing for dose sparing to the rectum, bladder, and sigmoid. A 1 : 1 solution of saline and contrast for filling material enables easy contouring for image-guided HDR with minimal artefacts

High resolution (3 Tesla) MRI-guided conformal brachytherapy for cervical cancer: consequences of different high-risk CTV sizes

Purpose: To evaluate conventional brachytherapy (BT) plans using dose-volume parameters and high resolution (3 Tesla) MRI datasets, and to quantify dosimetric benefits and limitations when MRI-guided, conformal BT (MRIG-CBT) plans are generated. Material and methods: Fifty-five clinical high-dose-rate BT plans from 14 cervical cancer patients were retrospectively studied. All conventional plans were created using MRI with titanium tandem-and-ovoid applicator (T&O) for delivery. For each conventional plan, a MRIG-CBT plan was retrospectively generated using hybrid inverse optimization. Three categories of high risk (HR)-CTV were considered based on volume: non-bulky (\u3c 20 cc), low-bulky (\u3e 20 cc and \u3c 40 cc) and bulky (≥ 40 cc). Dose-volume metrics of D90 of HR-CTV and D2cc and D0.1cc of rectum, bladder, and sigmoid colon were analyzed. Results: Tumor coverage (HR-CTV D90) of the conventional plans was considerably affected by the HR-CTV size. Sixteen percent of the plans covered HR-CTV D90 with the prescription dose within 5%. At least one OAR had D2cc values over the GEC-ESTRO recommended limits in 52.7% of the conventional plans. MRIG-CBT plans showed improved target coverage for HR-CTV D90 of 98 and 97% of the prescribed dose for non-bulky and low-bulky tumors, respectively. No MRIG-CBT plans surpassed the D2cc limits of any OAR. Only small improvements (D90 of 80%) were found for large targets (\u3e 40 cc) when using T&O applicator approach. Conclusions: MRIG-CBT plans displayed considerable improvement for tumor coverage and OAR sparing over conventional treatment. When the HR-CTV volume exceeded 40 cc, its improvements were diminished when using a conventional intracavitary applicator

Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer

PurposeThe study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP).MethodsThe DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed.ResultsThere was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and ‘dead’ group in their Kaplan-Meyer curves (p = 0.019).ConclusionDeep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT.SummaryWhile current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing