This study aimed to analyze gender differences in rank, career duration, publication productivity, and research funding among radiation oncologists at U.S. academic institutions.
For 82 domestic ...academic radiation oncology departments, the authors identified current faculty and recorded their academic rank, degree, and gender. The authors recorded bibliographic metrics for physician faculty from a commercially available database (Scopus, Elsevier BV), including numbers of publications from 1996 to 2012 and h-indices. The authors then concatenated these data with National Institutes of Health (NIH) funding per Research Portfolio Online Reporting Tools. The authors performed descriptive and correlative analyses, stratifying by gender and rank.
Of 1,031 faculty, 293 (28%) women and 738 (72%) men, men had a higher median m-index, 0.58 (range 0-3.23) versus 0.47 (0-2.5) (P < .05); h-index, 8 (0-59) versus 5 (0-39) (P < .05); and publication number, 26 (0-591) versus 13 (0-306) (P < .05). Men were more likely to be senior faculty and receive NIH funding. After stratifying for rank, these differences were largely nonsignificant. On multivariate analysis, there were correlations between gender, career duration and academic position, and h-index (P < .01).
Determinants of a successful career in academic medicine are multifactorial. Data from radiation oncologists show a systematic gender association, with fewer women achieving senior faculty rank. However, women achieving seniority have productivity metrics comparable to those of male counterparts. This suggests that early career development and mentorship of female faculty may narrow productivity disparities.
Radiation therapy (RT) is an essential component of effective cancer care and is used across nearly all cancer types. The delivery of RT is becoming more precise through rapid advances in both ...computing and imaging. The direct integration of magnetic resonance imaging (MRI) with linear accelerators represents an exciting development with the potential to dramatically impact cancer research and treatment. These impacts extend beyond improved imaging and dose deposition. Real-time MRI-guided RT is actively transforming the work flows and capabilities of virtually every aspect of RT. It has the opportunity to change entirely the delivery methods and response assessments of numerous malignancies. This review intends to approach the topic of MRI-based RT guidance from a vendor neutral and international perspective. It also aims to provide an introduction to this topic targeted towards oncologists without a speciality focus in RT. Speciality implications, areas for physician education and research opportunities are identified as they are associated with MRI-guided RT. The uniquely disruptive implications of MRI-guided RT are discussed and placed in context. We further aim to describe and outline important future changes to the speciality of radiation oncology that will occur with MRI-guided RT. The impacts on RT caused by MRI guidance include target identification, RT planning, quality assurance, treatment delivery, training, clinical workflow, tumour response assessment and treatment scheduling. In addition, entirely novel research areas that may be enabled by MRI guidance are identified for future investigation.
•We present a review on the current state of MR-guided RT (RT).•Speciality implications, areas for physician education and research opportunities are identified.•Some impacts include target identification, quality assurance and tumour response assessment.
•Intensity modulated proton therapy (IMPT) is a sophisticated mode of proton therapy.•Dosimetric studies have demonstrated the superiority of IMPT over IMRT to improve dose sparing of organs in ...located in the head and neck.•There is clinical evidence that IMPT can translate to toxicity reductions for patients with HNCs.•This review will discuss existing literature and future directions of IMPT use for HNCs.
Radiation therapy plays an integral role in the management of head and neck cancers (HNCs). While most HNC patients have historically been treated with photon-based radiation techniques such as intensity modulated radiation therapy (IMRT), there is a growing awareness of the potential clinical benefits of proton therapy over IMRT in the definitive, postoperative and reirradiation settings given the unique physical properties of protons. Intensity modulated proton therapy (IMPT), also known as “pencil beam proton therapy,” is a sophisticated mode of proton therapy that is analogous to IMRT and an active area of investigation in cancer care. Multifield optimization IMPT allows for high quality plans that can target superficially located HNCs as well as large neck volumes while significantly reducing integral doses. Several dosimetric studies have demonstrated the superiority of IMPT over IMRT to improve dose sparing of nearby organs such as the larynx, salivary glands, and esophagus. Evidence of the clinical translation of these dosimetric advantages has been demonstrated with documented toxicity reductions (such as decreased feeding tube dependency) after IMPT for patients with HNCs. While there are relative challenges to IMPT planning that exist today such as particle range uncertainties and high sensitivity to anatomical changes, ongoing investigations in image-guidance techniques and robust optimization methods are promising. A systematic approach towards utilizing IMPT and additional prospective studies are necessary in order to more accurately estimate the clinical benefit of IMPT over IMRT and passive proton therapy on a case-by-case basis for patients with sub-site specific HNCs.
Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that ...imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables-HPV status and tumor volume-were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol.
Reirradiation of head and neck (H&N) cancer is a clinical challenge. Proton radiation therapy (PRT) offers dosimetric advantages for normal tissue sparing and may benefit previously irradiated ...patients. Here, we report our initial experience with the use of PRT for H&N reirradiation, with focus on clinical outcomes and toxicity.
We retrospectively reviewed the records of patients who received H&N reirradiation with PRT from April 2011 through June 2015. Patients reirradiated with palliative intent or without prior documentation of H&N radiation therapy were excluded. Radiation-related toxicities were recorded according to the Common Terminology Criteria for Adverse Events Version 4.0.
The conditions of 60 patients were evaluated, with a median follow-up time of 13.6 months. Fifteen patients (25%) received passive scatter proton therapy (PSPT), and 45 (75%) received intensity modulated proton therapy (IMPT). Thirty-five patients (58%) received upfront surgery, and 44 (73%) received concurrent chemotherapy. The 1-year rates of locoregional failure-free survival, overall survival, progression-free survival, and distant metastasis-free survival were 68.4%, 83.8%, 60.1%, and 74.9%, respectively. Eighteen patients (30%) experienced acute grade 3 (G3) toxicity, and 13 (22%) required a feeding tube at the end of PRT. The 1-year rates of late G3 toxicity and feeding tube independence were 16.7% and 2.0%, respectively. Three patients may have died of reirradiation-related effects (1 acute and 2 late).
Proton beam therapy can be a safe and effective curative reirradiation strategy, with acceptable rates of toxicity and durable disease control.
A substantial barrier to the single- and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource ...systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, dose–volume histogram DVH-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future. A multisociety, multidisciplinary, multinational group of 57 members representing stake holders ranging from large academic centers to community clinics and vendors was assembled, including physicists, physicians, dosimetrists, and vendors. The stakeholder groups represented in the membership included the AAPM, American Society for Radiation Oncology (ASTRO), NRG Oncology, European Society for Radiation Oncology (ESTRO), Radiation Therapy Oncology Group (RTOG), Children's Oncology Group (COG), Integrating Healthcare Enterprise in Radiation Oncology (IHE-RO), and Digital Imaging and Communications in Medicine working group (DICOM WG); A nomenclature system for target and organ at risk volumes and DVH nomenclature was developed and piloted to demonstrate viability across a range of clinics and within the framework of clinical trials. The final report was approved by AAPM in October 2017. The approval process included review by 8 AAPM committees, with additional review by ASTRO, European Society for Radiation Oncology (ESTRO), and American Association of Medical Dosimetrists (AAMD). This Executive Summary of the report highlights the key recommendations for clinical practice, research, and trials.
•The paper describes the first challenge on head and neck tumor segmentation in PET/CT.•Training (n=201, 4 centers) and test sets (n=53, 1 unseen center) amount to 254 cases.•All ground truth ...segmentations underwent cleaning to ensure quality and homogeneity.•The winning team obtained a DSC of 0.759, showing a larg improvement over the baseline.•Additional post-challenge analyses (e.g. false positives analysis, ranking stability).
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This paper relates the post-analysis of the first edition of the HEad and neCK TumOR (HECKTOR) challenge. This challenge was held as a satellite event of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020, and was the first of its kind focusing on lesion segmentation in combined FDG-PET and CT image modalities. The challenge’s task is the automatic segmentation of the Gross Tumor Volume (GTV) of Head and Neck (H&N) oropharyngeal primary tumors in FDG-PET/CT images. To this end, the participants were given a training set of 201 cases from four different centers and their methods were tested on a held-out set of 53 cases from a fifth center. The methods were ranked according to the Dice Score Coefficient (DSC) averaged across all test cases. An additional inter-observer agreement study was organized to assess the difficulty of the task from a human perspective. 64 teams registered to the challenge, among which 10 provided a paper detailing their approach. The best method obtained an average DSC of 0.7591, showing a large improvement over our proposed baseline method and the inter-observer agreement, associated with DSCs of 0.6610 and 0.61, respectively. The automatic methods proved to successfully leverage the wealth of metabolic and structural properties of combined PET and CT modalities, significantly outperforming human inter-observer agreement level, semi-automatic thresholding based on PET images as well as other single modality-based methods. This promising performance is one step forward towards large-scale radiomics studies in H&N cancer, obviating the need for error-prone and time-consuming manual delineation of GTVs.
Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image ...biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.
Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition ...to fully autonomous transportation. Advances in each of these domains have led some to call AI the “fourth” industrial revolution 1. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.
Purpose
Online adaptive radiotherapy would greatly benefit from the development of reliable auto‐segmentation algorithms for organs‐at‐risk and radiation targets. Current practice of manual ...segmentation is subjective and time‐consuming. While deep learning‐based algorithms offer ample opportunities to solve this problem, they typically require large datasets. However, medical imaging data are generally sparse, in particular annotated MR images for radiotherapy. In this study, we developed a method to exploit the wealth of publicly available, annotated CT images to generate synthetic MR images, which could then be used to train a convolutional neural network (CNN) to segment the parotid glands on MR images of head and neck cancer patients.
Methods
Imaging data comprised 202 annotated CT and 27 annotated MR images. The unpaired CT and MR images were fed into a 2D CycleGAN network to generate synthetic MR images from the CT images. Annotations of axial slices of the synthetic images were generated by propagating the CT contours. These were then used to train a 2D CNN. We assessed the segmentation accuracy using the real MR images as test dataset. The accuracy was quantified with the 3D Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD) between manual and auto‐generated contours. We benchmarked the approach by a comparison to the interobserver variation determined for the real MR images, as well as to the accuracy when training the 2D CNN to segment the CT images.
Results
The determined accuracy (DSC: 0.77±0.07, HD: 18.04±12.59mm, MSD: 2.51±1.47mm) was close to the interobserver variation (DSC: 0.84±0.06, HD: 10.85±5.74mm, MSD: 1.50±0.77mm), as well as to the accuracy when training the 2D CNN to segment the CT images (DSC: 0.81±0.07, HD: 13.00±7.61mm, MSD: 1.87±0.84mm).
Conclusions
The introduced cross‐modality learning technique can be of great value for segmentation problems with sparse training data. We anticipate using this method with any nonannotated MRI dataset to generate annotated synthetic MR images of the same type via image style transfer from annotated CT images. Furthermore, as this technique allows for fast adaptation of annotated datasets from one imaging modality to another, it could prove useful for translating between large varieties of MRI contrasts due to differences in imaging protocols within and between institutions.
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