Primary Results of NRG-RTOG1106/ECOG-ACRIN 6697: A Randomized Phase II Trial of Individualized Adaptive (chemo)Radiotherapy Using Midtreatment F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Stage III Non-Small Cell Lung Cancer.

Purpose: NRG-RTOG0617 demonstrated a detrimental effect of uniform high-dose radiation in stage III non-small cell lung cancer. NRG-RTOG1106/ECOG-ACRIN6697 (ClinicalTrials.gov identifier: NCT01507428), a randomized phase II trial, studied whether midtreatment F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) can guide individualized/adaptive dose-intensified radiotherapy (RT) to improve and predict outcomes in patients with this disease.

Quality-of-life outcomes from NRG Oncology NSABP B-39/RTOG 0413: whole-breast irradiation vs accelerated partial-breast irradiation after breast-conserving surgery.

Background: NRG Oncology NSABP B-39/RTOG 0413 compared whole-breast irradiation (WBI) to accelerated partial-breast irradiation (APBI). APBI was not equivalent to WBI in local tumor control. Secondary outcome was quality of life (QOL).

Radiotherapy Plus Cisplatin With or Without Lapatinib for Non-Human Papillomavirus Head and Neck Carcinoma: A Phase 2 Randomized Clinical Trial.

Importance: Patients with locally advanced non-human papillomavirus (HPV) head and neck cancer (HNC) carry an unfavorable prognosis. Chemoradiotherapy (CRT) with cisplatin or anti-epidermal growth factor receptor (EGFR) antibody improves overall survival (OS) of patients with stage III to IV HNC, and preclinical data suggest that a small-molecule tyrosine kinase inhibitor dual EGFR and ERBB2 (formerly HER2 or HER2/neu) inhibitor may be more effective than anti-EGFR antibody therapy in HNC.

Objective: To examine whether adding lapatinib, a dual EGFR and HER2 inhibitor, to radiation plus cisplatin for frontline therapy of stage III to IV non-HPV HNC improves progression-free survival (PFS).

Pathologic Complete Response and Clinical Outcomes in Patients With Localized Soft Tissue Sarcoma Treated With Neoadjuvant Chemoradiotherapy or Radiotherapy: The NRG/RTOG 9514 and 0630 Nonrandomized Clinical Trials.

Importance: Pathologic complete response (pCR) may be associated with prognosis in patients with soft tissue sarcoma (STS).

Objective: We sought to determine the prognostic significance of pCR on survival outcomes in STS for patients receiving neoadjuvant chemoradiotherapy (CT-RT) (Radiation Therapy Oncology Group [RTOG] 9514) or preoperative image-guided radiotherapy alone (RT, RTOG 0630) and provide a long-term update of RTOG 0630.

Design, Setting, And Participants: RTOG has completed 2 multi-institutional, nonrandomized phase 2 clinical trials for patients with localized STS.

Population, Clinical, and Scientific Impact of National Cancer Institute's National Clinical Trials Network Treatment Studies.

Purpose: In the United States, the National Cancer Institute National Cancer Clinical Trials Network (NCTN) groups have conducted publicly funded oncology research for 50 years. The combined impact of all adult network group trials has never been systematically examined.

Methods: We identified randomized, phase III trials from the adult NCTN groups, reported from 1980 onward, with statistically significant findings for ≥ 1 clinical, time-dependent outcomes.

Safety of Nivolumab Added to Chemoradiation Therapy Platforms for Intermediate and High-Risk Locoregionally Advanced Head and Neck Squamous Cell Carcinoma: RTOG Foundation 3504.

Purpose: Programmed death-1 immune checkpoint blockade improves survival of patients with recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), but the benefits of addition to (chemo)radiation for newly diagnosed patients with HNSCC remain unknown.

Methods And Materials: We evaluated the safety of nivolumab concomitant with 70 Gy intensity modulated radiation therapy and weekly cisplatin (arm 1), every 3-week cisplatin (arm 2), cetuximab (arm 3), or alone for platinum-ineligible patients (arm 4) in newly diagnosed intermediate- or high-risk locoregionally advanced HNSCC. Patients received nivolumab from 2 weeks prior to radiation therapy until 3 months post-radiation therapy.

Depatuxizumab mafodotin in EGFR-amplified newly diagnosed glioblastoma: A phase III randomized clinical trial.

Background: Approximately 50% of newly diagnosed glioblastomas (GBMs) harbor epidermal growth factor receptor gene amplification (EGFR-amp). Preclinical and early-phase clinical data suggested efficacy of depatuxizumab mafodotin (depatux-m), an antibody-drug conjugate comprised of a monoclonal antibody that binds activated EGFR (overexpressed wild-type and EGFRvIII-mutant) linked to a microtubule-inhibitor toxin in EGFR-amp GBMs.

Methods: In this phase III trial, adults with centrally confirmed, EGFR-amp newly diagnosed GBM were randomized 1:1 to radiotherapy, temozolomide, and depatux-m/placebo.

Preliminary efficacy and safety analysis: 12-month results in 83 patients using a novel approach of widefield radiation therapy for extensive skin field cancerization with or without keratinocyte cancers.

Purpose: Evaluate the use of widefield radiation therapy (RT) in the management of extensive skin field cancerization (ESFC) with/without keratinocyte cancer (KC).

Methods: The National Dermatology Radiation Oncology Registry is a multidisciplinary collaboration (dermatologists and radiation oncologists). It captures disease description, prior therapies, radiation prescription, clinical effect, skin cosmesis scores, and toxicity data.

Higher Radiation Dose to the Immune Cells Correlates with Worse Tumor Control and Overall Survival in Patients with Stage III NSCLC: A Secondary Analysis of RTOG0617.

: We hypothesized that the Effective radiation Dose to the Immune Cells (EDIC) in circulating blood is a significant factor for the treatment outcome in patients with locally advanced non-small-cell lung cancer (NSCLC). : This is a secondary study of a phase III trial, NRG/RTOG 0617, in patients with stage III NSCLC treated with radiation-based treatment. The EDIC was computed as equivalent uniform dose to the entire blood based on radiation doses to all blood-containing organs, with consideration of blood flow and fractionation effect.

MRI classification using semantic random forest with auto-context model.

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.

Methods: We applied a semantic classification random forest (SCRF) method which consists of a training stage and a segmentation stage.

Multi-organ auto-delineation in head-and-neck MRI for radiation therapy using regional convolutional neural network.

Magnetic resonance imaging (MRI) allows accurate and reliable organ delineation for many disease sites in radiation therapy because MRI is able to offer superb soft-tissue contrast. Manual organ-at-risk delineation is labor-intensive and time-consuming. This study aims to develop a deep-learning-based automated multi-organ segmentation method to release the labor and accelerate the treatment planning process for head-and-neck (HN) cancer radiotherapy.

Surgical Outcomes for Early Stage Non-small Cell Lung Cancer at Facilities With Stereotactic Body Radiation Therapy Programs.

Background: Patients undergoing surgery for early stage non-small cell lung cancer (NSCLC) may be at high risk for postoperative mortality. Access to stereotactic body radiation therapy (SBRT) may facilitate more appropriate patient selection for surgery.

Research Question: Is postoperative mortality associated with early stage NSCLC lower at facilities with higher use of SBRT?

Study Design And Methods: Patients with early stage NSCLC reported to the National Cancer Database between 2004 and 2015 were included.

Deep learning-based motion tracking using ultrasound images.

Purpose: Ultrasound (US) imaging is an established imaging modality capable of offering video-rate volumetric images without ionizing radiation. It has the potential for intra-fraction motion tracking in radiation therapy. In this study, a deep learning-based method has been developed to tackle the challenges in motion tracking using US imaging.

Learning-based synthetic dual energy CT imaging from single energy CT for stopping power ratio calculation in proton radiation therapy.

Objective: Dual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. This work presents a learning-based method to synthesize DECT images from SECT image for proton radiotherapy.

Methods: The proposed method uses a residual attention generative adversarial network.

Deep learning-based thoracic CBCT correction with histogram matching.

Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield unit (HU) values. Here, we propose a new deep learning-based method to generate synthetic CTs (sCT) from thoracic CBCTs.

Synthetic CT-aided multiorgan segmentation for CBCT-guided adaptive pancreatic radiotherapy.

Purpose: The delineation of organs at risk (OARs) is fundamental to cone-beam CT (CBCT)-based adaptive radiotherapy treatment planning, but is time consuming, labor intensive, and subject to interoperator variability. We investigated a deep learning-based rapid multiorgan delineation method for use in CBCT-guided adaptive pancreatic radiotherapy.

Methods: To improve the accuracy of OAR delineation, two innovative solutions have been proposed in this study.

Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey.

Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magnetic resonance imaging, QUS suffers from several major drawbacks: poor image quality and inter- and intra-observer variability. Therefore, there is a great need to develop automated methods to improve the image quality of QUS.

Automated delineation of head and neck organs at risk using synthetic MRI-aided mask scoring regional convolutional neural network.

Purpose: Auto-segmentation algorithms offer a potential solution to eliminate the labor-intensive, time-consuming, and observer-dependent manual delineation of organs-at-risk (OARs) in radiotherapy treatment planning. This study aimed to develop a deep learning-based automated OAR delineation method to tackle the current challenges remaining in achieving reliable expert performance with the state-of-the-art auto-delineation algorithms.

Methods: The accuracy of OAR delineation is expected to be improved by utilizing the complementary contrasts provided by computed tomography (CT) (bony-structure contrast) and magnetic resonance imaging (MRI) (soft-tissue contrast).

Knowledge-based radiation treatment planning: A data-driven method survey.

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 require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models.

Artificial intelligence in tumor subregion analysis based on medical imaging: A review.

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.

Fully automated segmentation of brain tumor from multiparametric MRI using 3D context deep supervised U-Net.

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 process and also more susceptible to intra- and inter-rater variabilities possibly leading to misclassification. To deal with this issue, this study aims to develop an automatic segmentation of brain tumor in MR images using deep learning.

Learning-based dose prediction for pancreatic stereotactic body radiation therapy using dual pyramid adversarial network.

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. However, its effectiveness on pancreatic cancer SBRT is yet to be fully explored due to limited investigations in the literature.

High through-plane resolution CT imaging with self-supervised deep learning.

CT images for radiotherapy planning are usually acquired in thick slices to reduce the 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 is proposed to synthesize high through-plane resolution CT images by learning from their high in-plane resolution features.