Clinical acceptability of fully automated external beam radiotherapy for cervical cancer with three different beam delivery techniques.

Purpose: To fully automate CT-based cervical cancer radiotherapy by automating contouring and planning for three different treatment techniques.

Methods: We automated three different radiotherapy planning techniques for locally advanced cervical cancer: 2D 4-field-box (4-field-box), 3D conformal radiotherapy (3D-CRT), and volumetric modulated arc therapy (VMAT). These auto-planning algorithms were combined with a previously developed auto-contouring system.

Automatic contouring QA method using a deep learning-based autocontouring system.

Purpose: To determine the most accurate similarity metric when using an independent system to verify automatically generated contours.

Methods: A reference autocontouring system (primary system to create clinical contours) and a verification autocontouring system (secondary system to test the primary contours) were used to generate a pair of 6 female pelvic structures (UteroCervix [uterus + cervix], CTVn [nodal clinical target volume (CTV)], PAN [para-aortic lymph nodes], bladder, rectum, and kidneys) on 49 CT scans from our institution and 38 from other institutions. Additionally, clinically acceptable and unacceptable contours were manually generated using the 49 internal CT scans.

Automatic contouring system for cervical cancer using convolutional neural networks.

Purpose: To develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancer patients.

Methods: An auto-contouring tool based on convolutional neural networks (CNN) was developed to delineate three cervical CTVs and 11 normal structures (seven OARs, four bony structures) in cervical cancer treatment for use with the Radiation Planning Assistant, a web-based automatic plan generation system. A total of 2254 retrospective clinical computed tomography (CT) scans from a single cancer center and 210 CT scans from a segmentation challenge were used to train and validate the CNN-based auto-contouring tool.

A dosimetric evaluation of IGART strategies for cervix cancer treatment.

Purpose: Image guided adaptive radiotherapy (IGART) strategies can be used to include the temporal aspects of radiotherapy treatment. A dosimetric evaluation of on- and off-line adaptive strategies are done in this study.

Methods: A library of equivalent uniform dose (EUD)-based Intensity Modulated Radiotherapy Treatment plans with incrementally increasing clinical target volume (CTV)-to-planning target volume (PTV) margins were developed for 10 patients.