Automated plan generation for prostate radiotherapy patients using deep learning and scripted optimization.

Background And Purpose: Treatment planning is a time-intensive task that could be automated. We aimed to develop a "single-click" workflow, fully deployed within a commercial treatment planning system (TPS), for autoplanning prostate radiotherapy treatment plans using predictions from a deep learning model (DLM).

Materials And Methods: Automatically generated treatment plans were created with a single script, executed from within a commercial TPS scripting environment, that performed two stages sequentially.

The effect of spatial resolution on deep learning classification of lung cancer histopathology.

Objective: The microscopic analysis of biopsied lung nodules represents the gold-standard for definitive diagnosis of lung cancer. Deep learning has achieved pathologist-level classification of non-small cell lung cancer histopathology images at high resolutions (0.5-2 µm/px), and recent studies have revealed tomography-histology relationships at lower spatial resolutions.

Automated conversion of Millennium-120 VMAT plans to HDMLC geometry: Software development and treatment of first patients.

Purpose: To provide plan backup resiliency for patients treated on a solitary high definition multileaf collimator (HDMLC) linac by developing a fully integrated Eclipse script, which converts patient plans initially optimized on Millennium-120 (M120) MLC to dosimetrically equivalent leaf motions for delivery on HDMLC. In the event of HDMLC machine downtime, affected patients can be transferred to Millennium-120 units, and their backup plan delivered without delay.

Methods: Write-enabled Eclipse scripting is leveraged to generate HDMLC treatment fields with control points parameterized to mimic apertures of an existing Millennium-120 VMAT plan.

Assessment of intrafraction motion for spine and non-spine bone metastases treated with image-guided stereotactic body radiotherapy without 6 degrees-of-freedom couch correction.

Stereotactic body radiotherapy (SBRT) planning target volume (PTV) margins are influenced by multiple factors. Data is limited on intrafraction motion in bone SBRT, particularly non-spine lesions. We analyzed intrafraction motion in bone SBRT patients treated on a standard treatment couch without 6 degrees-of-freedom (6-DOF) correction.

Automated prediction of dosimetric eligibility for hypofractionated prostate radiotherapy.

Clinical implementation of hypofractionated prostate radiotherapy (PROFIT trial, NCT003046759) represents an opportunity to significantly reduce the burden of treatment on the patient and clinic. However, efficacy was only demonstrated among the patient demographic who could meet the trial dose constraints and so it is necessary to emulate this triage step in clinical practice. The purpose of this study was to build a convenient tool to address the challenge of determining patient eligibility for hypofractionated treatment within the clinic.