Radio-morphology: Parametric shape-based features in radiotherapy.

Purpose: In radiotherapy, it is necessary to characterize dose over the patient anatomy to target areas and organs at risk. Current tools provide methods to describe dose in terms of percentage of volume and magnitude of dose, but are limited by assumptions of anatomical homogeneity within a region of interest (ROI) and provide a non-spatially aware description of dose. A practice termed radio-morphology is proposed as a method to apply anatomical knowledge to parametrically derive new shapes and substructures from a normalized set of anatomy, ensuring consistently identifiable spatially aware features of the dose across a patient set.

Improving prediction of surgical resectability over current staging guidelines in patients with pancreatic cancer who receive stereotactic body radiation therapy.

Purpose: For patients with localized pancreatic cancer (PC) with vascular involvement, prediction of resectability is critical to define optimal treatment. However, the current definitions of borderline resectable (BR) and locally advanced (LA) disease leave considerable heterogeneity in outcomes within these classifications. Moreover, factors beyond vascular involvement likely affect the ability to undergo resection.

Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy.

Objective: We explore whether a knowledge-discovery approach building a Classification and Regression Tree (CART) prediction model for weight loss (WL) in head and neck cancer (HNC) patients treated with radiation therapy (RT) is feasible.

Methods And Materials: HNC patients from 2007 to 2015 were identified from a prospectively collected database Oncospace. Two prediction models at different time points were developed to predict weight loss ≥5 kg at 3 months post-RT by CART algorithm: (1) during RT planning using patient demographic, delineated dose data, planning target volume-organs at risk shape relationships data and (2) at the end of treatment (EOT) using additional on-treatment toxicities and quality of life data.

Using Big Data Analytics to Advance Precision Radiation Oncology.

Big clinical data analytics as a primary component of precision medicine is discussed, identifying where these emerging tools fit in the spectrum of genomics and radiomics research. A learning health system (LHS) is conceptualized that uses clinically acquired data with machine learning to advance the initiatives of precision medicine. The LHS is comprehensive and can be used for clinical decision support, discovery, and hypothesis derivation.

Effects of perineural invasion on biochemical recurrence and prostate cancer-specific survival in patients treated with definitive external beam radiotherapy.

Objectives: Perineural invasion (PNI) has not yet gained universal acceptance as an independent predictor of adverse outcomes for prostate cancer treated with external beam radiotherapy (EBRT). We analyzed the prognostic influence of PNI for a large institutional cohort of prostate cancer patients who underwent EBRT with and without androgen deprivation therapy (ADT).

Material And Methods: We, retrospectively, reviewed prostate cancer patients treated with EBRT from 1993 to 2007 at our institution.

Very High-Risk Localized Prostate Cancer: Outcomes Following Definitive Radiation.

Purpose: Existing definitions of high-risk prostate cancer consist of men who experience significant heterogeneity in outcomes. As such, criteria that identify a subpopulation of National Comprehensive Cancer Network (NCCN) high-risk prostate cancer patients who are at very high risk (VHR) for poor survival outcomes following prostatectomy were recently developed at our institution and include the presence of any of the following disease characteristics: multiple NCCN high-risk factors, primary Gleason pattern 5 disease and/or ≥5 biopsy cores with Gleason sums of 8 to 10. Whether these criteria also apply to men undergoing definitive radiation is unclear, as is the optimal treatment regimen in these patients.

A data-mining framework for large scale analysis of dose-outcome relationships in a database of irradiated head and neck cancer patients.

Purpose: To develop a hypothesis-generating framework for automatic extraction of dose-outcome relationships from an in-house, analytic oncology database.

Methods: Dose-volume histograms (DVH) and clinical outcomes have been routinely stored to the authors' database for 684 head and neck cancer patients treated from 2007 to 2014. Database queries were developed to extract outcomes that had been assessed for at least 100 patients, as well as DVH curves for organs-at-risk (OAR) that were contoured for at least 100 patients.

A block matching-based registration algorithm for localization of locally advanced lung tumors.

Purpose: To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy.

Methods: Small (1 cm(3)), nonoverlapping image subvolumes ("blocks") were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform.

Localization accuracy from automatic and semi-automatic rigid registration of locally-advanced lung cancer targets during image-guided radiation therapy.

Purpose: To evaluate localization accuracy resulting from rigid registration of locally-advanced lung cancer targets using fully automatic and semi-automatic protocols for image-guided radiation therapy.

Methods: Seventeen lung cancer patients, fourteen also presenting with involved lymph nodes, received computed tomography (CT) scans once per week throughout treatment under active breathing control. A physician contoured both lung and lymph node targets for all weekly scans.

Tumor, lymph node, and lymph node-to-tumor displacements over a radiotherapy series: analysis of interfraction and intrafraction variations using active breathing control (ABC) in lung cancer.

Purpose: To estimate errors in soft tissue-based image guidance due to relative changes between primary tumor (PT) and affected lymph node (LN) position and volume, and to compare the results with bony anatomy-based displacements of PTs and LNs during radiotherapy of lung cancer.

Methods And Materials: Weekly repeated breath-hold computed tomography scans were acquired in 17 lung cancer patients undergoing radiotherapy. PTs and affected LNs were manually contoured on all scans after rigid registration.