Publications by authors named "Madalina Costea"
: To evaluate an end-to-end pipeline for normo-fractionated prostate-only and whole-pelvic cancer treatments that requires minimal human input and generates a machine-deliverable plan as an output. : In collaboration with TheraPanacea, a treatment planning pipeline was developed that takes as its input a planning CT with organs-at-risk (OARs) and planning target volume (PTV) contours, the targeted linac machine, and the prescription dose. The primary components are (i) dose prediction by a single deep learning model for both localizations and (ii) a direct aperture VMAT plan optimization that seeks to mimic the predicted dose.
View Article and Find Full Text PDFPurpose: To investigate the performance of 4 atlas-based (multi-ABAS) and 2 deep learning (DL) solutions for head-and-neck (HN) elective nodes (CTVn) automatic segmentation (AS) on CT images.
Material And Methods: Bilateral CTVn levels of 69 HN cancer patients were delineated on contrast-enhanced planning CT. Ten and 49 patients were used for atlas library and for training a mono-centric DL model, respectively.
Article Synopsis
- - The study aimed to evaluate the effectiveness of both four atlas-based and two deep learning methods for automatically segmenting organs-at-risk in head-and-neck treatments.
- - Results showed that deep learning algorithms generally outperformed atlas-based methods in accuracy, although one hybrid atlas-based approach sometimes matched the best performance of the deep learning algorithms.
- - While deep learning methods were faster and more efficient for manual corrections, significant dose differences were mostly noticed when organs were close to treatment targets, indicating careful consideration needed in contour placement.
Purpose: Automated planning techniques aim to reduce manual planning time and inter-operator variability without compromising the plan quality which is particularly challenging for head-and-neck (HN) cancer radiotherapy. The objective of this study was to evaluate the performance of an a priori-multicriteria plan optimization algorithm on a cohort of HN patients.
Methods: A total of 14 nasopharyngeal carcinoma (upper-HN) and 14 "middle-lower indications" (lower-HN) previously treated in our institution were enrolled in this study.