Towards quantitative ionizing radiation acoustic imaging (iRAI) for radiation dose measurement: Validation from simulations to experiments.

Background: In clinical radiation therapy (RT), accurately quantifying the delivered radiation dose to the targeted tumors and surrounding tissues is essential for evaluating treatment outcomes. Ionizing radiation acoustic imaging (iRAI), a novel passive and non-invasive imaging technique, has the potential to provide real-time in vivo radiation dose mapping during RT. However, current iRAI technology does not account for spatial variations in the detection sensitivity of the ultrasound transducer used to capture the iRAI signals, leading to significant errors in dose mapping.

The Biophysics of Flash Radiotherapy: Tools for Measuring Tumor and Normal Tissues Microenvironment.

Ultra-high dose rate radiotherapy known as Flash radiotherapy (FLASH-RT) offers tremendous opportunities to improve the therapeutic ratio of radiotherapy by sparing the normal tissue while maintaining similar tumoricidal efficacy. However, the underlying biophysical basis of the FLASH effect remains under active investigation with several proposed mechanisms involving oxygen depletion, altered free-radical chemistry, and differential biological responses. This article provides an overview of available experimental and computational tools that can be utilized to probe the tumor and normal tissue microenvironment.

Artificial Intelligence for Multiscale Spatial Analysis in Oncology: Current Applications and Future Implications.

Artificial intelligence (AI) and its machine learning and deep learning algorithms have shown promise in oncological practice. Spatial information analysis in the context of cancer is crucial for its diagnosis and treatment because it can provide an understanding of tumor-microenvironment interactions and reveal insights into response to treatment. AI tools can analyze spatial information at multiple scales, highlighting key disease, clinical, and genetic phenotypes that may reveal underlying mechanisms and molecular markers of response and resistance within the tumor and its microenvironment.

Factors impacting cardiac dose and overall survival in post-operative non-small cell lung cancer patients.

Background: The LungART trial randomized NSCLC patients with N2 disease to adjuvant chemotherapy ± radiotherapy. They found that patients who received radiotherapy had reduced overall survival and claimed that heart dose was the etiological factor, though without offering proof. Consequently, we evaluated cardiac dose and overall survival (OS) in a large cohort of patients treated with adjuvant radiotherapy for N2 disease.

The Role of PET Image Quantification in Modern Radiation Therapy.

Radiation therapy has established itself as a key modality for definitive and palliative treatment of oncologic patients. Advanced radiation therapy techniques use sophisticated treatment planning strategies to generate 3 dimensional dose distributions, which closely fit areas of cancer and decrease the radiation exposure to normal tissues. Quantitative PET imaging with multiple tracers (eg, fluorodeoxyglucose, FMISO, and prostatic-specific membrane antigen) is playing an increasing role in radiotherapy (RT) planning before, during, and following therapy administration for improved target delineation and response assessment.

Early Treatment Engagement and Long-Term Smoking Abstinence Among Women With Cervical Intraepithelial Neoplasia or Cervical Cancer.

Introduction: Cancer survivors who quit smoking have improved treatment response and decreased mortality. Although data indicate that greater tobacco cessation treatment engagement leads to better cessation outcomes in cancer survivors, little is known about how different tobacco treatment engagement patterns influence long-term cessation success. To help address this issue, this study examined how tobacco treatment engagement patterns predict cessation outcomes through 18 months and identified baseline participant characteristics predictive of treatment engagement patterns.

CLONEID: A Framework for Longitudinal Integration of Phenotypic and Genotypic Data to Monitor and Steer Subclonal Dynamics.

Understanding how genetic and phenotypic diversity emerges and evolves within cancer cell populations is a fundamental challenge in cancer biology. CLONEID is a novel framework designed to organize and analyze clone-specific measures as structured time-series data. By integrating and monitoring genotypic and phenotypic experimental data over time, CLONEID facilitates hypothesis-driven and hypothesis-generating research in cancer biology.

Ionizing radiation acoustic and ultrasound dual-modality imaging for visualization of dose on anatomical structures during radiotherapy.

The aim of this study is to visualize the radiation dose on anatomical structures during radiation therapy (RT) by mapping radiation dose deposition and tracking anatomical structures simultaneously. A dual-modality volumetric imaging system, which combines ionizing radiation acoustic imaging (iRAI) and ultrasound (US) imaging, was developed to provide dose deposition and anatomical information in real-time during RT. The performance of the proposed system was first evaluated via experiments on tissue-mimicking phantoms driven by a custom motion stage.

Roadmap: medical physics technologies in brachytherapy.

Brachytherapy is a crucial modality of radiotherapy for cancer, known for its effectiveness in delivering high doses of radiation directly to tumours while sparing surrounding healthy tissues. Despite its clinical importance, recent years have witnessed a concerning decline in its utilization, which negatively impacts patient outcomes. This decline is attributed to several factors, with the inherent complexity of brachytherapy, fair reimbursement policies, and high dexterity being significant barriers.

Integrative Immune Signature of Complementary Circulating and Tumoral Biomarkers Maximizes the Predictive Power of Adjuvant Immunotherapeutic Benefits in High-risk Melanoma.

Purpose: Ipilimumab (IPI) improved outcomes for patients with high-risk melanoma compared with IFN-α2b in E1609, a phase III adjuvant trial. We hypothesized that combining candidate immune biomarkers in both tumor and circulating blood could generate a superior predictive biomarker signature.

Experimental Design: We conducted gene expression profiling on baseline tumors of patients treated with IPI and IFN.

Real-Time Dose-Guided Radiation Therapy.

Dramatic strides have been made in real-time adaptive radiation therapy, where treating single tumors as dynamic but rigid bodies has demonstrated a halving of toxicities for prostate cancer. However, the human body is much more complex than a rigid body. This review explores the ongoing development and future potential of dose-guided radiation therapy, where the three core process steps of volumetric imaging of the patient, dose accumulation, and dose-guided treatment adaptation occur quasi-continuously during treatment, fully accounting for the complexity of the dynamic human body.

Easy ensemble classifier-group and intersectional fairness and threshold (EEC-GIFT): a fairness-aware machine learning framework for lung cancer screening eligibility using real-world data.

Background: We use real-world data to develop a lung cancer screening (LCS) eligibility mechanism that is both accurate and free from racial bias.

Methods: Our data came from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial. We built a systematic fairness-aware machine learning framework by integrating a Group and Intersectional Fairness and Threshold (GIFT) strategy with an easy ensemble classifier-(EEC-) or logistic regression-(LR-) based model.

Does Time to Retreatment Matter? A Normal Tissue Complication Probability Model to Predict Radionecrosis After Repeat Stereotactic Radiosurgery for Recurrent Brain Metastases Incorporating Time-dependent Discounted Dose.

Purpose: To develop and compare normal tissue complication probability (NTCP) models for recurrent brain metastases (BMs) treated with repeat single-fraction stereotactic radiosurgery (SRS), considering time-dependent discounted prior dose.

Methods And Materials: We developed three NTCP models (M0, M1-retreat, and M1-combo models) of BMs treated with GammaKnife-based SRS. The maximum dose is 0.

Intricacies of human-AI interaction in dynamic decision-making for precision oncology.

AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.

Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.

Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.

Dose Planning and Radiation Optimization for Thoracic Conventional, Twice Daily, and Stereotactic Radiation Therapy: A Delphi Consensus From a National Survey of Practitioners.

Purpose: We sent surveys to a large number of radiation oncologists with active thoracic cancer practices and applied the Delphi method over 3 rounds to generate consensus dose-volume histogram metrics. We used these results to create consensus-based organs-at-risk dose constraints and target goal templates for practical implementation.

Methods And Materials: In this institutional review board-approved study, data were collected using REDCap electronic data capture on a secure server.

Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.

Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly impacted the imaging and treatment of cancers. Radiomics is the process of extracting high-dimensional features from medical images.

Cell identity revealed by precise cell cycle state mapping links data modalities.

Several methods for cell cycle inference from sequencing data exist and are widely adopted. In contrast, methods for classification of cell cycle state from imaging data are scarce. We have for the first time integrated sequencing and imaging derived cell cycle pseudo-times for assigning 449 imaged cells to 693 sequenced cells at an average resolution of 3.

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI without a lesion segmentation prerequisite. Materials and Methods In this exploratory study, 154 cases of biopsy-proven DCIS (25 upgraded at surgery and 129 not upgraded) were selected consecutively from a retrospective cohort of preoperative DCE MRI in women with a mean age of 59 years at time of diagnosis from 2012 to 2022. Binary classification was implemented with convolutional neural network (CNN)-long short-term memory (LSTM) architectures benchmarked against traditional CNNs without manual segmentation of the lesions.

Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability.

The majority of deep learning models in medical image analysis concentrate on single snapshot timepoint circumstances, such as the identification of current pathology on a given image or volume. This is often in contrast to the diagnostic methodology in radiology where presumed pathologic findings are correlated to prior studies and subsequent changes over time. For multiple sclerosis (MS), the current body of literature describes various forms of lesion segmentation with few studies analyzing disability progression over time.

Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy.

Background: Adaptive treatment strategies that can dynamically react to individual cancer progression can provide effective personalized care. Longitudinal multi-omics information, paired with an artificially intelligent clinical decision support system (AI-CDSS) can assist clinicians in determining optimal therapeutic options and treatment adaptations. However, AI-CDSS is not perfectly accurate, as such, clinicians' over/under reliance on AI may lead to unintended consequences, ultimately failing to develop optimal strategies.

Modeling non-genetic information dynamics in cells using reservoir computing.

Virtually all cells use energy-driven, ion-specific membrane pumps to maintain large transmembrane gradients of Na, K, Cl, Mg, and Ca, but the corresponding evolutionary benefit remains unclear. We propose that these gradients enable a dynamic and versatile biological system that acquires, analyzes, and responds to environmental information. We hypothesize that environmental signals are transmitted into the cell by ion fluxes along pre-existing gradients through gated ion-specific membrane channels.