Walter J Curran

• MD
• Managing Director at Emory University

This paper surveys the data‐driven dose prediction methods investigated for knowledge‐based planning (KBP) in the last decade. These methods were classified into two major categories—traditional KBP methods and deep‐learning (DL) methods—according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that requ...

Purpose Owing to histologic complexities of brain tumors, its diagnosis requires the use of multimodalities to obtain valuable structural information so that brain tumor subregions can be properly delineated. In current clinical workflow, physicians typically perform slice‐by‐slice delineation of brain tumor subregions, which is a time‐consuming pr...

Treatment planning for pancreatic cancer stereotactic body radiation therapy (SBRT) is very challenging owing to vast spatial variations and close proximity of many organs-at-risk. Recently, deep learning (DL)-based methods have been applied in dose prediction tasks of various treatment sites with the aim of relieving planning challenges. Limited i...

Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI‐based tumor subregion analysis in medical imaging. We summarize the latest AI‐based methods for tumor subregion analysis and their applications. Specifically, we c...

CT images for radiotherapy planning are usually acquired in thick slice to reduce imaging dose, especially for pediatric patients, and to lessen the need for contouring and treatment planning on more slices. However, low through-plane resolution may degrade the accuracy of dose calculations. In this paper, a self-supervised deep learning workflow i...

6014 Background: Chemoradiation (CRT) with cis or anti-EGFR Ab has been shown to improve survival of patients with stage III-IV HNC. Since Lap, a dual EGFR and HER2 inhibitor, has shown effectiveness with CRT in a pilot non-HPV HNC cohort, the RTOG Foundation launched a phase II trial to test the hypothesis that adding Lap to the RT-cis for frontli...

8538 Background: Patients undergoing surgery for early-stage non-small cell lung cancer (NSCLC) may be at high-risk for post-operative mortality. Access to stereotactic body radiation therapy (SBRT) offers a less invasive alternative for this population that may facilitate more appropriate patient selection for surgery. Methods: An analysis of all...

Purpose Ultrasound (US) imaging has been widely used in diagnosis, image‐guided intervention, and therapy, where high‐quality three‐dimensional (3D) images are highly desired from sparsely acquired two‐dimensional (2D) images. This study aims to develop a deep learning‐based algorithm to reconstruct high‐resolution (HR) 3D US images only reliant on...

Background: It is challenging to differentiate air and bone on MR images of conventional sequences due to their low contrast. We propose to combine semantic feature extraction under auto-context manner into random forest to improve reasonability of the MRI segmentation for MRI-based radiotherapy treatment planning or PET attention correction. Met...

Purpose: Glioblastoma, the most common malignant brain tumor, was associated with a median survival of <1 year in the pre-temozolomide (TMZ) era. Despite advances in molecular and genetic profiling studies identifying several predictive biomarkers, none has been translated into routine clinical use. Our aim was to investigate the prognostic signif...

Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the f...

Organ delineation is crucial to diagnosis and therapy, while is also labor-intensive and observer-dependent. Dual energy CT (DECT) provides additional image contrast than conventional single energy CT (SECT), which may facilitate automatic organ segmentation. This work aims to develop an automatic multi-organ segmentation approach using deep learni...

Purpose Current prostate brachytherapy uses transrectal ultrasound images for implant guidance, where contours of the prostate and organs‐at‐risk are necessary for treatment planning and dose evaluation. This work aims to develop a deep learning‐based method for male pelvic multi‐organ segmentation on transrectal ultrasound images. Methods We deve...

The delineation of the prostate and organs-at-risk (OARs) is fundamental to prostate radiation treatment planning, but is currently labor-intensive and observer-dependent. We aimed to develop an automated computed tomography (CT)-based multi-organ (bladder, prostate, rectum, left and right femoral heads) segmentation method for prostate radiation t...

Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specific...

Purpose Cardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast‐to‐noise ratio and speckle noise of the echocardiographic images. Manual segmentation is subject to interobserver variability and is to...

Purpose Radiation dose to specific cardiac substructures, such as the atria and ventricles, has been linked to post‐treatment toxicity and has shown to be more predictive of these toxicities than dose to the whole heart. A deep learning‐based algorithm for automatic generation of these contours is proposed to aid in either retrospective or prospect...

MRI-only treatment planning is highly desirable in current proton radiation therapy workflow due to its appealing advantages such as bypassing MR-CT co-registration, avoiding x-ray CT exposure dose and reduced medical cost. However, MRI alone cannot provide stopping power ratio (SPR) information for dose calculations. Given that dual energy CT (DEC...

Purpose Dual-energy computed tomography (DECT) has been used to derive relative stopping power (RSP) maps by obtaining the energy dependence of photon interactions. The DECT-derived RSP maps could potentially be compromised by image noise levels and the severity of artifacts when using physics-based mapping techniques. This work presents a noise-ro...

A non-rigid MR-TRUS image registration framework is proposed for prostate interventions. The registration framework consists of a convolutional neural networks (CNN) for MR prostate segmentation, a CNN for TRUS prostate segmentation and a point-cloud based network for rapid 3D point cloud matching. Volumetric prostate point clouds were generated fr...

Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MR...

Purpose Postoperative radiosurgery (SRS) is associated with up to 30% risk of subsequent leptomeningeal disease (LMD). Radiographic pattern of LMD (classical “sugarcoating” [cLMD] vs. nodular [nLMD]) in this setting has been shown to be prognostic. However, the association of these findings with neurologic death (ND) is not well described. Methods...

This chapter reviews recent developments of generative adversarial networks (GAN)-based methods for medical and biomedical image synthesis tasks. These methods are classified into conditional GAN and Cycle-GAN according to the network architecture designs. For each category, a literature survey is given, which covers discussions of the network arch...

https://doi.org/10.1007/s00330-020-07482-5

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinic...

Purpose/Objective Phase I clinical trials have established low-dose, whole-lung radiotherapy (LD-RT) as safe for patients with COVID-19-related pneumonia. By focally dampening cytokine hyperactivation, LD-RT may improve disease outcomes through immunomodulation. Methods and Materials Patients with COVID-19-related pneumonia were treated with 1.5 G...

Background Immune checkpoint blockade (ICB) targeting programmed cell death protein 1 and cytotoxic T lymphocyte-associated protein 4 has achieved modest clinical activity as salvage therapy in relapsed small cell lung cancer (SCLC). We conducted this signal-finding study to assess the efficacy of ICB with or without radiation in relapsed SCLC. Me...

RTOG 3508/AbbVie M13-813/INTELLANCE-1 was a phase 3 trial of depatuximab-mafodotin (depatux-m, formerly ABT-414) that accrued 639 patients with EGFR-amplified newly diagnosed GBM. At the pre-specified interim OS analysis, the futility criteria were met and there was no survival benefit from adding depatux-m to SOC. Pre-specified secondary NCF analy...

Background and purpose Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in prostate cancer could potentially improve tumor control. The purpose of this study was to develop a method to accurately register multiparametric magnetic resonance imaging (MRI) with CBCT images for improved DIL delineation, treatment planning, and...

The purpose of this study it to develop a deep learning method for thyroid delineation with high accuracy, efficiency, and robustness in noncontrast-enhanced head and neck CTs. The cross-sectional analysis consisted of six tests, including randomized the cross-validation and hold-out experiments, tests of prediction accuracy between cancer and beni...

Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment delivery of lung stereotactic body radiation therapy (SBRT) for accurate and active motion management. In this proof-of-concept study, we propose a novel generative adversarial network integrated with percep...

Purpose Complementary information obtained from multiple contrasts of tissue facilitates physicians assessing, diagnosing and planning treatment of a variety of diseases. However, acquiring multiple contrasts magnetic resonance images (MRI) for every patient using multiple pulse sequences is time‐consuming and expensive, where, medical image synthe...

Purpose: Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer pat...

Purpose Automatic breast ultrasound (ABUS) imaging has become an essential tool in breast cancer diagnosis since it provides complementary information to other imaging modalities. Lesion segmentation on ABUS is a prerequisite step of breast cancer computer‐aided diagnosis (CAD). This work aims to develop a deep learning‐based method for breast tumo...

Background: Radiotherapy (RT) has been shown to stimulate an antitumor immune response in irradiated tumors as well as unirradiated distant sites (abscopal effect). Previous studies have demonstrated a role for the tumor-draining lymph node (LN) in mediating an anti-programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) stimulated antitumor i...

Background Individuals of advanced age with comorbidities face a higher risk of death from coronavirus disease 2019 (COVID‐19), especially once they are ventilator‐dependent. Respiratory decline in patients with COVID‐19 is precipitated by a lung‐mediated aberrant immune cytokine storm. Low‐dose lung radiation was used to treat pneumonia in the pre...

The US cancer cooperative groups (cooperative groups) were founded in the 1950s to establish a standing infrastructure to conduct multi‐institutional cancer clinical trials. Initially funded almost entirely by the US National Cancer Institute (NCI), over the years, the research conducted by the Cooperative Groups has evolved to meet the demands of...

This paper surveys the data-driven dose prediction approaches introduced for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories according to their methods and techniques of utilizing previous knowledge: traditional KBP methods and deep-learning-based methods. Previous studies that required geo...

Introduction: Individuals with advanced age and comorbidities face risk of death from COVID-19, especially once ventilator-dependent, precipitated by an immune cytokine storm in the lungs. Lymphocytes, a mediator of cytokine storms, are exquisitely sensitive to ionizing radiation. Low doses of radiation therapy (LD-RT) were used to treat infectious...

Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative magnetic resonance (MR) image analysis in daily clinical practice. Although having no severe impact on visual diagnosis, the INU can highly degrade the performance of automatic quantitative analysis such as segme...

The rapid expansion of machine learning is offering a new wave of opportunities for nuclear medicine. This paper reviews applications of machine learning for the study of attenuation correction (AC) and low-count image reconstruction in quantitative positron emission tomography (PET). Specifically, we present the developments of machine learning me...

The segmentation of neoplasms is an important part of radiotherapy treatment planning, monitoring disease progression, and predicting patient outcome. In the brain, functional magnetic resonance imaging (MRI) like dynamic susceptibility contrast enhanced (DSCE) or T1-weighted dynamic contrast enhanced (DCE) perfusion MRI are important tools for dia...

Background: Safety of whole-lung low-dose radiation therapy (LD-RT) for COVID-19 pneumonia has been established in two phase I trials. By focally dampening pulmonary cytokine hyperactivation, LD-RT may improve outcomes in hospitalized and oxygen-dependent COVID-19 patients. Methods: Patients with COVID-19 pneumonia were treated with 1.5 Gy whole-lu...

Purpose Because the manual contouring process is labor‐intensive and time‐consuming, segmentation of organs‐at‐risk (OARs) is a weak link in radiotherapy treatment planning process. Our goal was to develop a synthetic MR (sMR)‐aided dual pyramid network (DPN) for rapid and accurate head and neck multi‐organ segmentation in order to expedite the tre...