Investigation of Radiation-Induced Toxicity in Head and Neck Cancer Patients through Radiomics and Machine Learning: A Systematic Review

Background. Radiation-induced toxicity represents a crucial concern in oncological treatments of patients affected by head and neck neoplasms, due to its impact on survivors' quality of life. Published reports suggested the potential of radiomics combined with machine learning methods in the prediction and assessment of radiation-induced toxicities, supporting a tailored radiation treatment management. In this paper, we present an update of the current knowledge concerning these modern approaches. Materials and Methods. A systematic review according to PICO-PRISMA methodology was conducted in MEDLINE/PubMed and EMBASE databases until June 2019. Studies assessing the use of radiomics combined with machine learning in predicting radiation-induced toxicity in head and neck cancer patients were specifically included. Four authors (two independently and two in concordance) assessed the methodological quality of the included studies using the Radiomic Quality Score (RQS). The overall score for each analyzed study was obtained by the sum of the single RQS items; the average and standard deviation values of the authors' RQS were calculated and reported. Results. Eight included papers, presenting data on parotid glands, cochlea, masticatory muscles, and white brain matter, were specifically analyzed in this review. Only one study had an average RQS was ≤ 30% (50%), while 3 studies obtained a RQS almost ≤ 25%. Potential variability in the interpretations of specific RQS items could have influenced the inter-rater agreement in specific cases. Conclusions. Published radiomic studies provide encouraging but still limited and preliminary data that require further validation to improve the decision-making processes in preventing and managing radiation-induced toxicities

INTEGRATION OF BIOMEDICAL IMAGING AND TRANSLATIONAL APPROACHES FOR MANAGEMENT OF HEAD AND NECK CANCER

The aim of the clinical component of this work was to determine whether the currently available clinical imaging tools can be integrated with radiotherapy (RT) platforms for monitoring and adaptation of radiation dose, prediction of tumor response and disease outcomes, and characterization of patterns of failure and normal tissue toxicity in head and neck cancer (HNC) patients with potentially curable tumors. In Aim 1, we showed that the currently available clinical imaging modalities can be successfully used to adapt RT dose based-on dynamic tumor response, predict oncologic disease outcomes, characterize RT-induced toxicity, and identify the patterns of disease failure. We used anatomical MRIs for the RT dose adaptation purpose. Our findings showed that after proper standardization of the immobilization and image acquisition techniques, we can achieve high geometric accuracy. These images can then be used to monitor the shrinkage of tumors during RT and optimize the clinical target volumes accordingly. Our results also showed that this MR-guided dose adaptation technique has a dosimetric advantage over the standard of care and was associated with a reduction in normal tissue doses that translated into a reduction of the odds of long-term RT-induced toxicity. In the second aim, we used quantitative MRIs to determine its benefit for prediction of oncologic outcomes and characterization of RT-induced normal tissue toxicity. Our findings showed that delta changes of apparent diffusion coefficient parameters derived from diffusion-weighted images at mid-RT can be used to predict local recurrence and recurrence free-survival. We also showed that Ktrans and Ve vascular parameters derived from dynamic contrast-enhanced MRIs can characterize the mandibular areas of osteoradionecrosis. In the final clinical aim, we used CT images of recurrence and baseline CT planning images to develop a methodology and workflow that involves the application of deformable image registration software as a tool to standardize image co-registration in addition to granular combined geometric- and dosimetric-based failure characterization to correctly attribute sites and causes of locoregional failure. We then successfully applied this methodology to identify the patterns of failure following postoperative and definitive IMRT in HNC patients. Using this methodology, we showed that most recurrences occurred in the central high dose regions for patients treated with definitive IMRT compared with mainly non-central high dose recurrences after postoperative IMRT. We also correlated recurrences with pretreatment FDG-PET and identified that most of the central high dose recurrences originated in an area that would be covered by a 10-mm margin on the volume of 50% of the maximum FDG uptake. In the translational component of this work, we integrated radiomic features derived from pre-RT CT images with whole-genome measurements using TCGA and TCIA data. Our results demonstrated a statistically significant associations between radiomic features characterizing different tumor phenotypes and different genomic features. These findings represent a promising potential towards non-invasively tract genomic changes in the tumor during treatment and use this information to adapt treatment accordingly. In the final project of this dissertation, we developed a high-throughput approach to identify effective systemic agents against aggressive head and neck tumors with poor prognosis like anaplastic thyroid cancer. We successfully identified three candidate drugs and performed extensive in vitro and in vivo validation using orthotopic and PDX models. Among these drugs, HDAC inhibitor and LBH-589 showed the most effective tumor growth inhibition that can be used in future clinical trials

Oral Cancer

Oral cancer is a common disease with a high incidence of morbidity and mortality. Despite technological advancements in diagnosis and treatment, the five-year survival rate is low. Researchers worldwide are attempting to identify new and novel methods for early diagnosis and better treatment of oral cancer to improve survival rate and quality of life post recovery. This book examines current concepts in oral cancer and emphasizes future perspectives for diagnosis and treatment of disease for better clinical outcomes and patient care

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

Texture Analysis Platform for Imaging Biomarker Research

abstract: The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope. Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers. This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Quantifying, Understanding and Predicting Differences Between Planned and Delivered Dose to Organs at Risk in Head & Neck Cancer Patients Undergoing Radical Radiotherapy to Promote Intelligently Targeted Adaptive Radiotherapy

Introduction: Radical radiotherapy (RT) is an effective but toxic treatment for head and neck cancer (HNC). Contemporary radiotherapy techniques sculpt dose to target disease and avoid organs at risk (OARs), but anatomical change during treatment mean that the radiation dose delivered to the patient – delivered dose (DA), is different to that anticipated at planning – planned dose (DP). Modifying the RT plan during treatment – Adaptive Radiotherapy (ART) – could mitigate these risks by reducing dose to OARs. However, clinical data to guide patient selection for, and timing of ART, are for lacking. Methods: 337 patients with HNC were recruited to the Cancer Research UK VoxTox study. Demographic, disease and treatment data were collated, and both DP and DA to organs at risk (OARs) were computed from daily megavoltage CT image guidance scans, using an open-source deformable image registration package (Elastix). Toxicity data were prospectively collected. Relationships between DP, DA and late toxicities were investigated with univariate, and logistic regression normal tissue complication probability (NTCP) modelling approaches. A sub-study of VoxTox recruited 18 patients who had MRI scans before RT fractions 1, 6, 16, and 26. Changes in salivary gland volumes and relative apparent diffusion coefficient (ADC) values were measured and related to toxicity events. Results: Spinal cord dose differences were small, and not predicted by weight loss or shape change. Mean DA to all other OARs was higher than DP; factors predicting higher DA included primary disease site, concomitant therapy, shape change and advanced neck disease. Nine patients (3.7%) saw DA>DP by 2Gy to more than half of the OARs assessed. These patients all had received bilateral neck RT for N-stage 2b oropharyngeal cancer. Strong uni- and multivariate relationships between OAR dose and toxicity were seen. Differences between DA and DP-based dose-toxicity models were minimal, and not statistically significant. On MRI, both parotid and submandibular glands shrank during treatment, whilst relative ADC rose. Relationships with toxicity were inconclusive. Conclusions: Small differences between OAR DP and DA mean that DA-based toxicity prediction models confer negligible additional benefit at the population level. Factors such as primary disease sub-site, concomitant systemic therapy, staging and shape change may help to select the patients that do develop clinically significant dose differences, and would benefit most from ART for toxicity reduction

LABRAD : Vol 48, Issue 1 - July 2023

Editorial Articles Toxicology In Public Health: Mitigating Risk, Fostering Well-Being Predictive And Prognostic Biomarkers in Histopathology Cancer Registry and Public Health Surveillance - Role of Pathologists Diagnosis of Celiac Disease DQA1*02,05; DQB1*02,03:02 (DQ2&DQ8) in patients Magnifying The Clinical Value of Expert Review and Reassessment Urine Cytology: A Screening and Surveillance Test An Overview of Convalescent Plasma for The Treatment of COVID-19 Role of Imaging in Public Health Care Microsatellite Instability (MSI) Testing MRI Liver with Hepatocyte-Specific Contrast Agent Basics and Role of Cytopathology Bone Tumors: From Timely Diagnosis to Improved Patient Outcome Differentiating Between Dengue Fever and Malaria Using The Hematological Parameters in Complete Blood Count Coronary CT for Calcium Scoring Screening for Breast Cancer Meet the Managers of Pathology Unveiling the Dangers of Primary Amoebic Encephalitis.” Importance of Surveillance for Antifungal Drug Resistance: The Story of C. Auris Chronic pulmonary aspergillosis diagnosis; role of laboratory and radiology in improving case detection Molecular Screening for Non BCR ABL1 Myeloproliferative Neoplasms Soft Tissue Sarcoma: Patient Centred Approach for Functional Outcome Precision Therapy Unleashed: Unlocking Personalized Medicine with Therapeutic Drug Monitoring The Best of the Past Happenings at Pathology Unraveling the AKU Department of Pathology and Laboratory Medicine’s Online Universe - a Report by the Departmental PathCom Committee Polaroid Tests in Focus Therapeutic Drug Monitoring Information for Clinicianshttps://ecommons.aku.edu/labrad/1039/thumbnail.jp

EANM dosimetry committee recommendations for dosimetry of 177Lu-labelled somatostatin-receptor- and PSMA-targeting ligands

The purpose of the EANM Dosimetry Committee is to provide recommendations and guidance to scientists and clinicians on patient-specific dosimetry. Radiopharmaceuticals labelled with lutetium-177 (177Lu) are increasingly used for therapeutic applications, in particular for the treatment of metastatic neuroendocrine tumours using ligands for somatostatin receptors and prostate adenocarcinoma with small-molecule PSMA-targeting ligands. This paper provides an overview of reported dosimetry data for these therapies and summarises current knowledge about radiation-induced side effects on normal tissues and dose-effect relationships for tumours. Dosimetry methods and data are summarised for kidneys, bone marrow, salivary glands, lacrimal glands, pituitary glands, tumours, and the skin in case of radiopharmaceutical extravasation. Where applicable, taking into account the present status of the field and recent evidence in the literature, guidance is provided. The purpose of these recommendations is to encourage the practice of patient-specific dosimetry in therapy with 177Lu-labelled compounds. The proposed methods should be within the scope of centres offering therapy with 177Lu-labelled ligands for somatostatin receptors or small-molecule PSMA.</p