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. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

When accountability meets power: realizing sexual and reproductive health and rights.

This paper addresses a critical concern in realizing sexual and reproductive health and rights through policies and programs - the relationship between power and accountability. We examine accountability strategies for sexual and reproductive health and rights through the lens of power so that we might better understand and assess their actual working. Power often derives from deep structural inequalities, but also seeps into norms and beliefs, into what we 'know' as truth, and what we believe about the world and about ourselves within it. Power legitimizes hierarchy and authority, and manufactures consent. Its capillary action causes it to spread into every corner and social extremity, but also sets up the possibility of challenge and contestation.Using illustrative examples, we show that in some contexts accountability strategies may confront and transform adverse power relationships. In other contexts, power relations may be more resistant to change, giving rise to contestation, accommodation, negotiation or even subversion of the goals of accountability strategies. This raises an important question about measurement. How is one to assess the achievements of accountability strategies, given the shifting sands on which they are implemented?We argue that power-focused realist evaluations are needed that address four sets of questions about: i) the dimensions and sources of power that an accountability strategy confronts; ii) how power is built into the artefacts of the strategy - its objectives, rules, procedures, financing methods inter alia; iii) what incentives, disincentives and norms for behavior are set up by the interplay of the above; and iv) their consequences for the outcomes of the accountability strategy. We illustrate this approach through examples of performance, social and legal accountability strategies.

Beyond measurement: the drivers of disrespect and abuse in obstetric care.

Concerns about disrespect and abuse (D&A) experienced by women during institutional birth have become critical to the discourse on maternal health. The rapid growth of the field from diverse points of origin has given rise to multiple and, at times, confusing interpretations of D&A, pointing to the need for greater clarity in the concepts themselves. Furthermore, attention to measurement of the problem has been excessive when viewed in relation to the small amount of work on critical drivers of disrespect and abuse. This paper raises some key issues of conceptualisation and measurement for the field, puts forward a working definition, and explores two critical drivers of D&A - intersecting social and economic inequality, and the institutional structures and processes that frame the practice of obstetric care. By identifying gaps and raising questions about the deeper causes of D&A, we point to potentially fruitful directions for research and action.

Biophysical mechanisms of MRI signal frequency contrast in multiple sclerosis.

Phase images obtained with gradient echo MRI provide image contrast distinct from T1- and T2-weighted images. It is commonly assumed that the local contribution to MRI signal phase directly relates to local bulk tissue magnetic susceptibility. Here, we use Maxwell's equations and Monte Carlo simulations to provide theoretical background to the hypothesis that the local contribution to MRI signal phase does not depend on tissue bulk magnetic susceptibility but tissue magnetic architecture--distribution of magnetic susceptibility inclusions (lipids, proteins, iron, etc.) at the cellular and subcellular levels. Specifically, we show that the regular longitudinal structures forming cylindrical axons (myelin sheaths and neurofilaments) can be locally invisible in phase images. Contrary to an expectation that the phase contrast in multiple sclerosis lesions should always increase in degree along with worsening of lesion severity (which happens for all known MR magnitude-based contrast mechanisms), we show that phase contrast can actually disappear with extreme tissue destruction. We also show that the phase contrast in multiple sclerosis lesions could be altered without loss of nervous system tissue, which happens in mild injury to the myelin sheaths or axonal neurofilaments. Moreover, we predict that the sign of phase contrast in multiple sclerosis lesions indicates the predominant type of tissue injury-myelin damage (positive sign) vs. axonal neurofilament damage (negative sign). Therefore, our theoretical and experimental results shed light on understanding the relationship between gradient echo MRI signal phase and multiple sclerosis pathology.

Towards a better tomorrow: addressing intersectional gender power relations to eradicate inequities in maternal health.

An equity lens to maternal health has typically focused on assessing the differences in coverage and use of healthcare services and critical interventions. While this approach is important, we argue that healthcare experiences, dignity, rights, justice, and well-being are fundamental components of high quality and person-centred maternal healthcare that must also be considered. Looking at differences across one dimension alone does not reflect how fundamental drivers of maternal health inequities-including racism, ethnic or caste-based discrimination, and gendered power relations-operate. In this paper, we describe how using an intersectionality approach to maternal health can illuminate how power and privilege (and conversely oppression and exclusion) intersect and drive inequities. We present an intersectionality-informed analysis on antenatal care quality to illustrate the advantages of this approach, and what is lost in its absence. We reviewed and mapped equity-informed interventions in maternal health to existing literature to identify opportunities for improvement and areas for innovation. The gaps and opportunities identified were then synthesised to propose recommendations on how to apply an intersectionality lens to maternal health research, programmes, and policies.

Exploring published and novel pre-treatment CT and PET radiomics to stratify risk of progression among early-stage non-small cell lung cancer patients treated with stereotactic radiation.

PURPOSE: Disease progression after definitive stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) occurs in 20-40% of patients. Here, we explored published and novel pre-treatment CT and PET radiomics features to identify patients at risk of progression. MATERIALS/METHODS: Published CT and PET features were identified and explored along with 15 other CT and PET features in 408 consecutively treated early-stage NSCLC patients having CT and PET < 3 months pre-SBRT (training/set-aside validation subsets: n = 286/122). Features were associated with progression-free survival (PFS) using bootstrapped Cox regression (Bonferroni-corrected univariate predictor: p ≤ 0.002) and only non-strongly correlated predictors were retained (|Rs|<0.70) in forward-stepwise multivariate analysis. RESULTS: Tumor diameter and SUVmax were the two most frequently reported features associated with progression/survival (in 6/20 and 10/20 identified studies). These two features and 12 of the 15 additional features (CT: 6; PET: 6) were candidate PFS predictors. A re-fitted model including diameter and SUVmax presented with the best performance (c-index: 0.78; log-rank p-value < 0.0001). A model built with the two best additional features (CTspiculation1 and SUVentropy) had a c-index of 0.75 (log-rank p-value < 0.0001). CONCLUSIONS: A re-fitted pre-treatment model using the two most frequently published features - tumor diameter and SUVmax - successfully stratified early-stage NSCLC patients by PFS after receiving SBRT.

Simulating the Potential of Model-Based Individualized Prescriptions for Ultracentral Lung Tumors.

Purpose: The use of stereotactic body radiation therapy for ultracentral lung tumors is limited by increased toxicity. We hypothesized that using published normal tissue complication probability (NTCP) and tumor control probability (TCP) models could improve the therapeutic ratio between tumor control and toxicity. A proposed model-based approach was applied to virtually replan early-stage non-small cell lung cancer (NSCLC) tumors. Methods and Materials: The analysis included 63 patients with ultracentral NSCLC tumors treated at our center between 2008 and 2017. Along with current clinical constraints, additional NTCP model-based criteria, including for grade 3+ radiation pneumonitis (RP3+) and grade 2+ esophagitis, were implemented using 4 different fractionation schemes. Scaled dose distributions resulting in the highest TCP without violating constraints were selected (optimal plan [Planopt]). Planopt predictions were compared with the observed local control and toxicities. Results: The observed 2-year local control rate was 72% (95% CI, 57%-88%) compared with 87% (range, 6%-93%) for Planopt TCP. Thirty-nine patients had Planopt with TCP > 80%, and 14 patients had Planopt TCP < 50%. The Planopt NTCPs for RP3+ were reduced by nearly half compared with patients' observed RP3+. The RP3+ NTCP was the most frequent reason for TCP of Planopt < 80% (14/24 patients), followed by grade 2+ esophagitis NTCP (5/24 patients) due to larger tumors (>40 cc vs ≤40 cc; P = .002) or a shorter tumor to esophagus distance (≥5 cm vs <5 cm; P < .001). Conclusions: We demonstrated the potential for model-based prescriptions to yield higher TCP while respecting NTCP for patients with ultracentral NSCLC. Individualizing treatments based on NTCP- and TCP-driven simulations halved the predicted relative to the observed rates of RP3+. Our simulations also identified patients whose TCP could not be improved without violating NTCP due to larger tumors or a near tumor to esophagus proximity.

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. ROE can be used natively with MATLAB and is additionally made accessible in GNU Octave and Python, eliminating the need for commercial licenses. It provides a curated library of published and validated predictive models and incorporates clinical restrictions on normal tissue outcomes. ROE additionally provides batch-mode tools to evaluate and select among different fractionation schemes and analyze radiotherapy outcomes across patient cohorts. CONCLUSION: ROE is an open-source, GPL-copyrighted tool for interactive exploration of the dose-response relationship to aid in radiotherapy planning. We demonstrate its potential clinical relevance in (1) improving patient awareness by quantifying the risks and benefits of a given treatment protocol (2) assessing the potential for dose escalation across patient cohorts and (3) estimating accrual rates of new protocols.

External validation of pulmonary radiotherapy toxicity models for ultracentral lung tumors.

Introduction: Pulmonary toxicity is dose-limiting in stereotactic body radiation therapy (SBRT) for tumors that abut the proximal bronchial tree (PBT), esophagus, or other mediastinal structures. In this work we explored published models of pulmonary toxicity following SBRT for such ultracentral tumors in an independent cohort of patients. Methods: The PubMed database was searched for pulmonary toxicity models. Identified models were tested in a cohort of patients with ultracentral lung tumors treated between 2008 and 2017 at one large center (N = 88). This cohort included 60 % primary and 40 % metastatic tumors treated to 45 Gy in 5 fractions (fx), 50 Gy in 5 fx, 60 Gy in 8 fx, or 60 Gy in 15 fx prescribed as 100 % dose to PTV. Results: Seven published NTCP models from two studies were identified. The NTCP models utilized PBT max point dose (Dmax), D0.2 cm3, V65, V100, and V130. Within the independent cohort, the ≥ grade 3 toxicity and grade 5 toxicity rates were 18 % and 7-10 %, respectively, and the Dmax models best described pulmonary toxicity. The Dmax to 0.1 cm3 model was better calibrated and had increased steepness compared to the Dmax model. A re-planning study minimizing PBT 0.1 cm3 to below 122 Gy in EQD23 (for a 10 % ≥grade 3 pulmonary toxicity) was demonstrated to be completely feasible in 4/6 patients, and dose to PBT 0.1 cm3 was considerably lowered in all six patients. Conclusions: Pulmonary toxicity models were identified from two studies and explored within an independent ultracentral lung tumor cohort. A modified Dmax to 0.1 cm3 PBT model displayed the best performance. This model could be utilized as a starting point for rationally constructed airways constraints in ultracentral patients treated with SBRT or hypofractionation.

Acute Lymphoblastic Leukemia presenting as a Pathologic Fibular Fracture.

The authors present a case of a 2-year-old girl with left ankle pain. On examination, there was tenderness but no sign of superficial swelling, erythema, or deformity. Imaging studies revealed a heterogeneous lytic lesion in the distal diaphysis of the left fibula, causing a pathologic fracture. The patient was treated with ankle splinting, analgesia, and referred to a pediatric orthopedic physician. Ultimately the diagnosis of acute lymphoblastic leukemia (ALL) was made. The authors present the significance of discerning skeletal abnormalities and orthopedic pain as the initial manifestation of leukemia.

Acute-on-Chronic Subdural Hematoma Secondary to Falls Due to Alcoholism.

The authors present a case of a 58-year-old male who is a daily drinker, experiencing bitemporal headaches starting one week prior to seeking medical attention. The patient's physical examination and vital signs exhibited no irregularities. Imaging studies revealed an acute-on-chronic left subdural hematoma but no intracranial arterial thrombosis or significant stenosis. The patient was managed conservatively due to his intact mental status and did well. The authors discuss alcohol use as a predisposing factor for intracerebral hemorrhage due to the increased risk for head trauma.

A scoping review of the impact of organisational factors on providers and related interventions in LMICs: Implications for respectful maternity care.

We have limited understanding of the organisational issues at the health facility-level that impact providers and care as it relates to mistreatment in childbirth, especially in low- and middle-income countries (LMICs). By extension, it is not clear what types of facility-level organisational changes or changes in working environments in LMICs could support and enable respectful maternity care (RMC). While there has been relatively more attention to health system pressures related to shortages of staff and other resources as key barriers, other organisational challenges may be less explored in the context of RMC. This scoping review aims to consolidate evidence to address these gaps. We searched literature published in English between 2000-2021 within Scopus, PubMed, Google Scholar and ScienceDirect databases. Study selection was two-fold. Maternal health articles articulating an organisational issue at the facility- level and impact on providers and/or care in an LMIC setting were included. We also searched for literature on interventions but due to the limited number of related intervention studies in maternity care specifically, we expanded intervention study criteria to include all medical disciplines. Organisational issues captured from the non-intervention, maternal health studies, and solutions offered by intervention studies across disciplines were organised thematically and to establish linkages between problems and solutions. Of 5677 hits, 54 articles were included: 41 non-intervention maternal healthcare studies and 13 intervention studies across all medical disciplines. Key organisational challenges relate to high workload, unbalanced division of work, lack of professional autonomy, low pay, inadequate training, poor feedback and supervision, and workplace violence, and these were differentially influenced by resource shortages. Interventions that respond to these challenges focus on leadership, supportive supervision, peer support, mitigating workplace violence, and planning for shortages. While many of these issues were worsened by resource shortages, medical and professional hierarchies also strongly underpinned a number of organisational problems. Frontline providers, particularly midwives and nurses, suffer disproportionately and need greater attention. Transforming institutional leadership and approaches to supervision may be particularly useful to tackle existing power hierarchies that could in turn support a culture of respectful care.

A summative content analysis of how programmes to improve the right to sexual and reproductive health address power.

INTRODUCTION: Power shapes all aspects of global health. The concept of power is not only useful in understanding the current situation, but it is also regularly mobilised in programmatic efforts that seek to change power relations. This paper uses summative content analysis to describe how sexual and reproductive health (SRH) programmes in low-income and middle-income countries explicitly and implicitly aim to alter relations of power. METHODS: Content analysis is a qualitative approach to analysing textual data; in our analysis, peer-reviewed articles that describe programmes aiming to alter power relations to improve SRH constituted the data. We searched three databases, ultimately including 108 articles. We extracted the articles into a spreadsheet that included basic details about the paper and the programme, including what level of the social ecological model programme activities addressed. RESULTS: The programmes reviewed reflect a diversity of priorities and approaches to addressing power, though most papers were largely based in a biomedical framework. Most programmes intervened at multiple levels simultaneously; some of these were 'structural' programmes that explicitly aimed to shift power relations, others addressed multiple levels using a more typical programme theory that sought to change individual behaviours and proximate drivers. This prevailing focus on proximate behaviours is somewhat mismatched with the broader literature on the power-related drivers of SRH health inequities, which explores the role of embedded norms and structures. CONCLUSION: This paper adds value by summarising what the academic public health community has chosen to test and research in terms of power relations and SRH, and by raising questions about how this corresponds to the significant task of effecting change in power relations to improve the right to SRH.

Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT.

Objective.Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process.Approach.CT scans of 242 head and neck (H&N) cancer patients acquired from 2004 to 2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded framework was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021.Main results. Medians and inter-quartile ranges of Dice similarity coefficients (DSC) computed on the retrospective testing set (N = 24) were 0.87 (0.85-0.89) for the masseters, 0.80 (0.79-0.81) for the medial pterygoids, 0.81 (0.79-0.84) for the larynx, and 0.69 (0.67-0.71) for the constrictor. Auto-segmentations, when compared to two sets of manual segmentations in 10 randomly selected scans, showed better agreement (DSC) with each observer than inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request viahttps://github.com/cerr/CERR/wiki/Auto-Segmentation-models.Significance.We developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.

Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617.

PURPOSE: Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (RTOG) 0617 dose escalation trial data. We hypothesized that the trial heart doses were higher than reported due to inconsistent and insufficient heart segmentation. We tested our hypothesis by comparing doses between deep-learning (DL) segmented hearts and trial hearts. METHODS AND MATERIALS: The RTOG 0617 data were downloaded from The Cancer Imaging Archive; the 442 patients with trial hearts and dose distributions were included. All hearts were resegmented using our DL pipeline and quality assured to meet the requirements for clinical implementation. Dose (V5%, V30%, and mean heart dose) was compared between the 2 sets of hearts (Wilcoxon signed-rank test). Each dose metric was associated with overall survival (Cox proportional hazards). Lastly, 18 volume similarity metrics were assessed for the hearts and correlated with |DoseDL - DoseRTOG0617| (linear regression; significance: P ≤ .0028; corrected for 18 tests). RESULTS: Dose metrics were significantly higher for DL hearts compared with trial hearts (eg, mean heart dose: 15 Gy vs 12 Gy; P = 5.8E-16). All 3 DL heart dose metrics were stronger overall survival predictors than those of the trial hearts (median, P = 2.8E-5 vs 2.0E-4). Thirteen similarity metrics explained |DoseDL - DoseRTOG0617|; the axial distance between the 2 centers of mass was the strongest predictor (CENTAxial; median, R2 = 0.47; P = 6.1E-62). CENTAxial agreed with the qualitatively identified inconsistencies in the superior direction. The trial's qualitative heart contouring score was not correlated with |DoseDL - DoseRTOG0617| (median, R2 = 0.01; P = .02) or with any of the similarity metrics (median, Rs = 0.13 [range, -0.22 to 0.31]). CONCLUSIONS: Using a coherent heart definition, as enabled through our open-source DL algorithm, the trial heart doses in RTOG 0617 were found to be significantly higher than previously reported, which may have led to an even more rapid mortality accumulation. Auto-segmentation is likely to reduce contouring and dose inconsistencies and increase the quality of clinical RT trials.

ASHA Kirana: when digital technology empowered front-line health workers.

This practice paper describes our experience of implementing accredited social health activists (ASHA) Kirana, a digital technology-enabled Maternal Clinical Assessment Tool (M-CAT) and how the ASHAs felt empowered in the process. M-CAT aimed to train ASHAs to collect data that assists doctors in identifying maternal risks, in Karnataka, India. Systematic clinical assessment is not common in rural public health institutions. High caseloads, a tendency to 'normalise' maternal risks, varied competence of doctors and task shifting to insufficiently trained cadres may be some contributing factors. M-CAT was a response to this challenge. ASHAs asked a set symptom-cluster-based questions during home visits that were analysed by software algorithms to generate reports for doctors. M-CAT was implemented in one primary health centre with a group of 14 ASHAs, 2 auxiliary nurse midwives and 349 pregnant and postpartum women over 4 months. Our team worked with the ASHAs to refine the tool and supported them with training, hands-on assistance and regular debrief meetings. By learning how to collect individual-level data that they could interpret and act on, the ASHAs felt empowered with new knowledge on maternal risks. Their perfunctory data collection at home visits changed to substantive interactions with women and families, during which they captured pertinent qualitative information. The information asymmetry between doctors and ASHAs reduced. ASHAs started taking proactive steps on early indications of maternal risks. They changed from being mere transmitters of information to active users of it. Thus, technology-driven initiatives that include empowerment as an objective can strengthen the role of front-line workers in health systems.

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

Purpose: The goal of this study is to develop innovative methods for identifying radiomic features that are reproducible over varying image acquisition settings. Approach: 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. The largest common network component between the two networks was tested on phantom data consisting of five cancer samples. To further investigate whether radiomic features found can identify phenotypes, we propose a k -means clustering algorithm coupled with the optimal mass transport theory. This approach following the regularized partial correlation network analysis was tested on CT scans from 77 head and neck squamous cell carcinoma (HNSCC) patients in the Cancer Imaging Archive (TCIA) and validated using an independent dataset. Results: A set of common radiomic features was found in relatively large network components between the resultant two partial correlation networks resulting from a cohort of lung cancer patients. The reliability and reproducibility of those radiomic features were further validated on phantom data using the Wasserstein distance. Further analysis using the network-based Wasserstein k -means algorithm on the TCIA HNSCC data showed that the resulting clusters separate tumor subsites as well as HPV status, and this was validated on an independent dataset. Conclusion: We showed that a network-based analysis enables identifying reproducible radiomic features and use of the selected set of features can enhance clustering results.

Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy.

BACKGROUND AND PURPOSE: Reducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased. MATERIALS AND METHODS: Auto-segmentation was based on purpose-built DL, and automated planning used our in-house system, ECHO. Treatment plans for ten HNC patients, treated with 2 Gy × 35 fractions, were optimized (ECHO0). Six manually segmented OARs were replaced with DL auto-segmentations and the plans re-optimized (ECHO1). In a third set of plans, mean doses for auto-segmented ipsilateral masseter and medial pterygoid (MIMean, MPIMean), derived from a trismus risk model, were implemented as dose-volume objectives (ECHO2). Clinical dose-volume criteria were compared between the two scenarios (ECHO0 vs. ECHO1; ECHO1 vs. ECHO2; Wilcoxon signed-rank test; significance: p < 0.01). RESULTS: Small systematic differences were observed between the doses to the six auto-segmented OARs and their manual counterparts (median: ECHO1 = 6.2 (range: 0.4, 21) Gy vs. ECHO0 = 6.6 (range: 0.3, 22) Gy; p = 0.007), and the ECHO1 plans provided improved normal tissue sparing across a larger dose-volume range. Only in the ECHO2 plans, all patients fulfilled both MIMean and MPIMean criteria. The population median MIMean and MPIMean were considerably lower than those suggested by the trismus model (ECHO0: MIMean = 13 Gy vs. ≤42 Gy; MPIMean = 29 Gy vs. ≤68 Gy). CONCLUSIONS: Automated treatment planning can efficiently incorporate new structures from DL auto-segmentation, which results in trismus risk sparing without deteriorating treatment plan quality. Auto-planning and deep learning auto-segmentation together provide a powerful platform to further improve treatment planning.

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. Until recently, however, no kernel methods for dealing with nonlinear data have been proposed via the Wasserstein distance. In this work, we develop a novel method to compute the L2-Wasserstein distance in reproducing kernel Hilbert spaces (RKHS) called kernel L2-Wasserstein distance, which is implemented using the kernel trick. The latter is a general method in machine learning employed to handle data in a nonlinear manner. We evaluate the proposed approach in identifying computed tomography (CT) slices with dental artifacts in head and neck cancer, performing unsupervised hierarchical clustering on the resulting Wasserstein distance matrix that is computed on imaging texture features extracted from each CT slice. We further compare the performance of kernel Wasserstein distance with alternatives including kernel Kullback-Leibler divergence we previously developed. Our experiments show that the kernel approach outperforms classical non-kernel approaches in identifying CT slices with artifacts.