Predicting the hypoxic volume of head and neck tumors from fluorodeoxyglucose positron emission tomography images using artificial intelligence.

Background And Purpose: Tumor hypoxia is linked to lower local control rates and increased distant disease progression during head and neck (HN) radiotherapy. F-fluoromisonidazole (F-FMISO) positron emission tomography (PET) imaging measured hypoxia can aid dose selection for HN patients, but its availability is limited. Hence, we tested the hypothesis that an artificial intelligence (AI) model could synthesize F-FMISO-like images from routinely acquired F-fluorodeoxyglucose (F-FDG) PET images in order to predict primary tumor or metastatic lymph node hypoxic volumes.

Artificial Intelligence Apps for Medical Image Analysis using pyCERR and Cancer Genomics Cloud.

This work introduces a user-friendly, cloud-based software framework for conducting Artificial Intelligence (AI) analyses of medical images. The framework allows users to deploy AI-based workflows by customizing software and hardware dependencies. The components of our software framework include the Python-native Computational Environment for Radiological Research (pyCERR) platform for radiological image processing, Cancer Genomics Cloud (CGC) for accessing hardware resources and user management utilities for accessing images from data repositories and installing AI models and their dependencies.

The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters.

Clinical and Dosimetric Risk Factors Associated With Radiation-Induced Lung Toxicities After Multiple Courses of Lung Stereotactic Body Radiation Therapy.

Purpose: Data are limited on radiation-induced lung toxicities (RILT) after multiple courses of lung stereotactic body radiation therapy (SBRT). We herein analyze a large cohort of patients to explore the clinical and dosimetric risk factors associated with RILT in such settings.

Methods And Materials: A single institutional database of patients treated with multiple courses of lung SBRT between January 2014 and December 2019 was analyzed.

ROE (Radiotherapy Outcomes Estimator): An open-source tool for optimizing radiotherapy prescriptions.

Background And Objectives: Radiotherapy prescriptions currently derive from population-wide guidelines established through large clinical trials. We provide an open-source software tool for patient-specific prescription determination using personalized dose-response curves.

Methods: We developed ROE, a plugin to the Computational Environment for Radiotherapy Research to visualize predicted tumor control and normal tissue complication simultaneously, as a function of prescription dose.

Interpretable Machine Learning for Choosing Radiation Dose-volume Constraints on Cardio-pulmonary Substructures Associated with Overall Survival in NRG Oncology RTOG 0617.

Purpose: Our objective was to use interpretable machine learning for choosing dose-volume constraints on cardiopulmonary substructures (CPSs) associated with overall survival (OS) in radiation therapy for locally advanced non-small cell lung cancer.

Methods And Materials: A total of 428 patients with non-small cell lung cancer were randomly divided into training/validation/test subsets (n = 230/149/49) in Radiation Therapy Oncology Group 0617. Manual or automated contouring was performed to segment CPSs, including heart, atria, ventricles, aorta, left/right ventricle/atrium (LV+RV+LA+RA), inferior/superior vena cava, pulmonary artery, and pericardium.

CT and cone-beam CT of ablative radiation therapy for pancreatic cancer with expert organ-at-risk contours.

We describe a dataset from patients who received ablative radiation therapy for locally advanced pancreatic cancer (LAPC), consisting of computed tomography (CT) and cone-beam CT (CBCT) images with physician-drawn organ-at-risk (OAR) contours. The image datasets (one CT for treatment planning and two CBCT scans at the time of treatment per patient) were collected from 40 patients. All scans were acquired with the patient in the treatment position and in a deep inspiration breath-hold state.

An Incognizant Glitch-pinnacle Polyliner Dissociation: A Case Report.

Introduction: Liner dissociation of the pinnacle total hip arthroplasty system is a rare but documented complication. Although a few reports are published internationally, to the best of our knowledge no cases have been documented from India so far.

Case Report: A 31-year-old male presented with failed femoral head fracture fixation for which total hip replacement was done.

Reproducibility of radiomic features using network analysis and its application in Wasserstein -means clustering.

The goal of this study is to develop innovative methods for identifying radiomic features that are reproducible over varying image acquisition settings. We propose a regularized partial correlation network to identify reliable and reproducible radiomic features. This approach was tested on two radiomic feature sets generated using two different reconstruction methods on computed tomography (CT) scans from a cohort of 47 lung cancer patients.

Predictive Modeling of Thoracic Radiotherapy Toxicity and the Potential Role of Serum Alpha-2-Macroglobulin.

To investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis using multifactorial predictive models. Baseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans.

Registering Study Analysis Plans (SAPs) Before Dissecting Your Data-Updating and Standardizing Outcome Modeling.

Public preregistration of study analysis plans (SAPs) is widely recognized for clinical trials, but adopted to a much lesser extent in observational studies. Registration of SAPs prior to analysis is encouraged to not only increase transparency and exactness but also to avoid positive finding bias and better standardize outcome modeling. Efforts to generally standardize outcome modeling, which can be based on clinical trial and/or observational data, have recently spurred.

Radiomic analysis identifies tumor subtypes associated with distinct molecular and microenvironmental factors in head and neck squamous cell carcinoma.

Purpose: To identify whether radiomic features from pre-treatment computed tomography (CT) scans can predict molecular differences between head and neck squamous cell carcinoma (HNSCC) using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA).

Methods: 77 patients from the TCIA with HNSCC had imaging suitable for analysis. Radiomic features were extracted and unsupervised consensus clustering was performed to identify subtypes.

Library of deep-learning image segmentation and outcomes model-implementations.

An open-source library of implementations for deep-learning-based image segmentation and outcomes models based on radiotherapy and radiomics is presented. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. Inclusion of deep-learning-based image segmentation and outcomes models in the same library provides a fully automated and reproduceable pipeline to estimate prognosis.

A novel kernel Wasserstein distance on Gaussian measures: An application of identifying dental artifacts in head and neck computed tomography.

The Wasserstein distance is a powerful metric based on the theory of optimal mass transport. It gives a natural measure of the distance between two distributions with a wide range of applications. In contrast to a number of the common divergences on distributions such as Kullback-Leibler or Jensen-Shannon, it is (weakly) continuous, and thus ideal for analyzing corrupted and noisy data.

Predictors of acute throat or esophageal patient reported pain during radiation therapy for head and neck cancer.

Background And Purpose: Acute pain during weekly radiotherapy (RT) to the head and neck is not well characterized. We studied dose-volume metrics and clinical variables that are plausibly associated with throat or esophageal pain as measured with a weekly questionnaire during RT.

Materials And Methods: We prospectively collected weekly patient-reported outcomes from 122 head and neck cancer patients during RT.

Technical Note: Extension of CERR for computational radiomics: A comprehensive MATLAB platform for reproducible radiomics research.

Purpose: Radiomics is a growing field of image quantitation, but it lacks stable and high-quality software systems. We extended the capabilities of the Computational Environment for Radiological Research (CERR) to create a comprehensive, open-source, MATLAB-based software platform with an emphasis on reproducibility, speed, and clinical integration of radiomics research.

Method: The radiomics tools in CERR were designed specifically to quantitate medical images in combination with CERR's core functionalities of radiological data import, transformation, management, image segmentation, and visualization.

Image-guided radiotherapy reduces the risk of under-dosing high-risk prostate cancer extra-capsular disease and improves biochemical control.

Background: To determine if reduced dose delivery uncertainty is associated with daily image-guidance (IG) and Prostate Specific Antigen Relapse Free Survival (PRFS) in intensity-modulated radiotherapy (IMRT) of high-risk prostate cancer (PCa).

Methods: Planning data for consecutive PCa patients treated with IMRT (n = 67) and IG-IMRT (n = 35) was retrieved. Using computer simulations of setup errors, we estimated the patient-specific uncertainty in accumulated treatment dose distributions for the prostate and for posterolateral aspects of the gland that are at highest risk for extra-capsular disease.

MRI features predictive of negative surgical margins in patients with HER2 overexpressing breast cancer undergoing breast conservation.

Here we develop a tool to predict resectability of HER2+ breast cancer at breast conservation surgery (BCS) utilizing features identified on preoperative breast MRI. We identified patients with HER2+ breast cancer who obtained pre-operative breast MRI and underwent BCS between 2002-2013. From the contoured tumor on pre-operative MRI, shape, histogram, and co-occurrence and size zone matrix texture features were extracted.

Investigating the Robustness Neighborhood Gray Tone Difference Matrix and Gray Level Co-occurrence Matrix Radiomic Features on Clinical Computed Tomography Systems Using Anthropomorphic Phantoms: Evidence From a Multivendor Study.

Objective: The aim of this study was to determine if optimized imaging protocols across multiple computed tomography (CT) vendors could result in reproducible radiomic features calculated from an anthropomorphic phantom.

Methods: Materials with varying degrees of heterogeneity were placed throughout the lungs of the phantom. Twenty scans of the phantom were acquired on 3 CT manufacturers with chest CT protocols that had optimized protocol parameters.

Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics.

In this study, we investigate the use of imaging feature-based outcomes research ('radiomics') combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted.

A multiple-image-based method to evaluate the performance of deformable image registration in the pelvis.

Deformable image registration (DIR) is essential for adaptive radiotherapy (RT) for tumor sites subject to motion, changes in tumor volume, as well as changes in patient normal anatomy due to weight loss. Several methods have been published to evaluate DIR-related uncertainties but they are not widely adopted. The aim of this study was, therefore, to evaluate intra-patient DIR for two highly deformable organs-the bladder and the rectum-in prostate cancer RT using a quantitative metric based on multiple image registration, the distance discordance metric (DDM).

Breast cancer molecular subtype classifier that incorporates MRI features.

Purpose: To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes.

Materials And Methods: This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR + (n = 95, 53.

Predictors of acute toxicities during definitive chemoradiation using intensity-modulated radiotherapy for anal squamous cell carcinoma.

Purpose: To identify clinical and dosimetric factors associated with acute hematologic and gastrointestinal (GI) toxicities during definitive therapy using intensity-modulated radiotherapy (IMRT) for anal squamous cell carcinoma (ASCC).

Materials And Methods: We retrospectively analyzed 108 ASCC patients treated with IMRT. Clinical information included age, gender, stage, concurrent chemotherapy, mitomycin (MMC) chemotherapy and weekly hematologic and GI toxicity during IMRT.

Dosimetric Predictors of Radiation-Induced Vaginal Stenosis After Pelvic Radiation Therapy for Rectal and Anal Cancer.

Purpose: Although vaginal stenosis (VS) is a recognized toxicity in women who receive pelvic radiation therapy (RT), the relationship between RT dose and the volume and extent of toxicity has not been analyzed. We modeled this relationship to identify predictors of VS.

Methods And Materials: We evaluated 54 women, aged 29 to 78 years, who underwent pelvic RT for rectal or anal cancer during 2008 to 2011 and were enrolled in a prospective study evaluating vaginal dilator use.