Invasive Nodal Staging via Endobronchial Ultrasound and Outcome in Patients Treated with Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer - Results from a Single Institution Study.

INTRODUCTION: Stereotactic body radiation therapy (SBRT) is an effective treatment for medically inoperable early-stage non-small cell lung cancer (NSCLC). The prognostic value of invasive nodal staging (INS) for patients undergoing SRBT has not been studied extensively. Herein, we report the impact of INS in addition to 18F-FDG-PET on treatment outcome for patients with NSCLC undergoing SBRT. MATERIALS AND METHODS: Patients with stage I/ II NSCLC who underwent SBRT were included with IRB approval. Clinical, dosimetric, and radiological data were obtained. Overall survival (OS), regional recurrence free survival (RRFS), local recurrence free survival (LRFS), and distant recurrence free survival (DRFS) were analyzed using Kaplan Meyer method. Univariable analysis (UVA) and multivariable analysis (MVA) were performed to assess the relationship between the variables and the outcomes. RESULTS: A total of 376 patients were included in the analysis. Median follow up was 43 months (IQ 32.6-45.8). Median OS, LRFS, RRFS, DRFS were 40, 32, 32, 33 months, respectively. The 5-year local, regional, and distant failure rates were 13.4%, 23.5% and 25.3%, respectively. The 1-year, 3-year and 5-year OS were 83.8%, 55.6%, and 36.3%, respectively. On MVA, INS was not a predictor of either improved overall or any recurrence free survival endpoints while larger tumor size, age, and adjusted Charleston co-morbidity index (aCCI) were significant for inferior LRFS, RRFS, and DRFS. CONCLUSION: Invasive nodal staging did not improve overall or recurrence free survival among patients with early-stage NSCLC treated with SBRT whereas older age, aCCI, and larger tumor size were significant predictors of LRFS, RRFS, and DRFS.

Artificial intelligence applications in prostate cancer.

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology assessment and diagnostic imaging interpretation, risk stratification (i.e., prognostication), and prediction of therapeutic benefit for personalized treatment recommendations. To date, multiple AI algorithms have been explored for prostate cancer to address automation of clinical workflow, integration of data from multiple domains in the decision-making process, and the generation of diagnostic, prognostic, and predictive biomarkers. While many studies remain within the pre-clinical space or lack validation, the last few years have witnessed the emergence of robust AI-based biomarkers validated on thousands of patients, and the prospective deployment of clinically-integrated workflows for automated radiation therapy design. To advance the field forward, multi-institutional and multi-disciplinary collaborations are needed in order to prospectively implement interoperable and accountable AI technology routinely in clinic.

Development and dissemination of structured hashtags for radiation oncology: Two-Year trends.

Purpose: For radiation oncology, social media is a favored communication platform, but it uses non-structured hashtags, which limits communication. In this work, we created a set of structured hashtags with key opinion leaders in radiation oncology, and we report on their use after two years post-deployment. Materials/Methods: Hashtags were created, voted on, and refined by crowdsourcing 38 international experts, including physicians, physicists, patients, and organizations from North America, Europe, and Australia. The finalized hashtag set was shared with the radiation oncology community in September 2019. The number of tweets for each hashtag was quantified via Symplur through December 2021. For the top five tweeted hashtags, we captured the number of yearly tweets in the pre-deployment and post-deployment periods from 09/01/2019 to 08/31/2021. Results: The initial 2019 list contained 39 hashtags organized into nine categories. The top five hashtags by total number of tweets were: #Radonc, #PallOnc, #MedPhys, #SurvOnc, and #SuppOnc. Six hashtags had less than 10 total tweets and were eliminated. Post-deployment, there was an increase in the yearly tweets, with the following number of tweets by the second year post-deployment: #RadOnc (98,189 tweets), #MedPhys (15,858 tweets), and #SurvOnc (6,361 tweets). Two popular radiation oncology-related hashtags were added because of increased use: #DEIinRO (1,603 tweets by year 2) and #WomenWhoCurie (7,212 tweets by year 2). Over the two years, hashtags were used mostly by physicians (131,625 tweets, 34.8%). Conclusion: We created and tracked structured social media hashtags in radiation oncology. These hashtags disseminate information among a diverse oncologic community. To maintain relevance, regular updates are needed.

Oligometastatic Non-Small Cell Lung Cancer: A Practical Review of Prospective Trials.

Oligometastatic non-small cell lung cancer (NSCLC) is an intermediate state between localized and widely metastatic NSCLC, where systemic therapy in combination with aggressive local therapy when feasible can yield a favorable outcome. While different societies have adopted different definitions for oligometastatic NSCLC, the feasibility of curative intent treatment remains a major determinant of the oligometastatic state. The management involves a multidisciplinary approach to identify such patients with oligometastatic stage, including the presence of symptomatic or potentially symptomatic brain metastasis, the presence of targetable mutations, and programmed death-ligand (PD-L1) expression. Treatment requires a personalized approach with the use of novel systemic agents such as tyrosine kinase inhibitors and immune checkpoint inhibitors with or without chemotherapy, and addition of local ablative therapy via surgery or stereotactic radiation therapy when appropriate.

Abdominopelvic MR to CT registration using a synthetic CT intermediate.

Accurate coregistration of computed tomography (CT) and magnetic resonance (MR) imaging can provide clinically relevant and complementary information and can serve to facilitate multiple clinical tasks including surgical and radiation treatment planning, and generating a virtual Positron Emission Tomography (PET)/MR for the sites that do not have a PET/MR system available. Despite the long-standing interest in multimodality co-registration, a robust, routine clinical solution remains an unmet need. Part of the challenge may be the use of mutual information (MI) maximization and local phase difference (LPD) as similarity metrics, which have limited robustness, efficiency, and are difficult to optimize. Accordingly, we propose registering MR to CT by mapping the MR to a synthetic CT intermediate (sCT) and further using it in a sCT-CT deformable image registration (DIR) that minimizes the sum of squared differences. The resultant deformation field of a sCT-CT DIR is applied to the MRI to register it with the CT. Twenty-five sets of abdominopelvic imaging data are used for evaluation. The proposed method is compared to standard MI- and LPD-based methods, and the multimodality DIR provided by a state of the art, commercially available FDA-cleared clinical software package. The results are compared using global similarity metrics, Modified Hausdorff Distance, and Dice Similarity Index on six structures. Further, four physicians visually assessed and scored registered images for their registration accuracy. As evident from both quantitative and qualitative evaluation, the proposed method achieved registration accuracy superior to LPD- and MI-based methods and can refine the results of the commercial package DIR when using its results as a starting point. Supported by these, this manuscript concludes the proposed registration method is more robust, accurate, and efficient than the MI- and LPD-based methods.

Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network.

Multi-modality imaging constitutes a foundation of precision medicine, especially in oncology where reliable and rapid imaging techniques are needed in order to insure adequate diagnosis and treatment. In cervical cancer, precision oncology requires the acquisition of 18F-labeled 2-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET), magnetic resonance (MR), and computed tomography (CT) images. Thereafter, images are co-registered to derive electron density attributes required for FDG-PET attenuation correction and radiation therapy planning. Nevertheless, this traditional approach is subject to MR-CT registration defects, expands treatment expenses, and increases the patient's radiation exposure. To overcome these disadvantages, we propose a new framework for cross-modality image synthesis which we apply on MR-CT image translation for cervical cancer diagnosis and treatment. The framework is based on a conditional generative adversarial network (cGAN) and illustrates a novel tactic that addresses, simplistically but efficiently, the paradigm of vanishing gradient vs. feature extraction in deep learning. Its contributions are summarized as follows: 1) The approach -termed sU-cGAN-uses, for the first time, a shallow U-Net (sU-Net) with an encoder/decoder depth of 2 as generator; 2) sU-cGAN's input is the same MR sequence that is used for radiological diagnosis, i.e. T2-weighted, Turbo Spin Echo Single Shot (TSE-SSH) MR images; 3) Despite limited training data and a single input channel approach, sU-cGAN outperforms other state of the art deep learning methods and enables accurate synthetic CT (sCT) generation. In conclusion, the suggested framework should be studied further in the clinical settings. Moreover, the sU-Net model is worth exploring in other computer vision tasks.

Transforming UTE-mDixon MR Abdomen-Pelvis Images Into CT by Jointly Leveraging Prior Knowledge and Partial Supervision.

Computed tomography (CT) provides information for diagnosis, PET attenuation correction (AC), and radiation treatment planning (RTP). Disadvantages of CT include poor soft tissue contrast and exposure to ionizing radiation. While MRI can overcome these disadvantages, it lacks the photon absorption information needed for PET AC and RTP. Thus, an intelligent transformation from MR to CT, i.e., the MR-based synthetic CT generation, is of great interest as it would support PET/MR AC and MR-only RTP. Using an MR pulse sequence that combines ultra-short echo time (UTE) and modified Dixon (mDixon), we propose a novel method for synthetic CT generation jointly leveraging prior knowledge as well as partial supervision (SCT-PK-PS for short) on large-field-of-view images that span abdomen and pelvis. Two key machine learning techniques, i.e., the knowledge-leveraged transfer fuzzy c-means (KL-TFCM) and the Laplacian support vector machine (LapSVM), are used in SCT-PK-PS. The significance of our effort is threefold: 1) Using the prior knowledge-referenced KL-TFCM clustering, SCT-PK-PS is able to group the feature data of MR images into five initial clusters of fat, soft tissue, air, bone, and bone marrow. Via these initial partitions, clusters needing to be refined are observed and for each of them a few additionally labeled examples are given as the partial supervision for the subsequent semi-supervised classification using LapSVM; 2) Partial supervision is usually insufficient for conventional algorithms to learn the insightful classifier. Instead, exploiting not only the given supervision but also the manifold structure embedded primarily in numerous unlabeled data, LapSVM is capable of training multiple desired tissue-recognizers; 3) Benefiting from the joint use of KL-TFCM and LapSVM, and assisted by the edge detector filter based feature extraction, the proposed SCT-PK-PS method features good recognition accuracy of tissue types, which ultimately facilitates the good transformation from MR images to CT images of the abdomen-pelvis. Applying the method on twenty subjects' feature data of UTE-mDixon MR images, the average score of the mean absolute prediction deviation (MAPD) of all subjects is 140.72 ± 30.60 HU which is statistically significantly better than the 241.36 ± 21.79 HU obtained using the all-water method, the 262.77 ± 42.22 HU obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method, and the 197.05 ± 76.53 HU obtained via the conventional SVM method. These results demonstrate the effectiveness of our method for the intelligent transformation from MR to CT on the body section of abdomen-pelvis.

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any information about photon attenuation, AC is needed in PET/MR systems in order to be quantitatively accurate and to meet qualification standards required for use in many multi-center trials. Existing MR-based synthetic CT generation methods either use advanced MR sequences that have long acquisition time and limited clinical availability or use matching of the MR images from a newly scanned subject to images in a library of MR-CT pairs which has difficulty in accounting for the diversity of human anatomy especially in patients that have pathologies. To address these deficiencies, we present a five-phase interlinked method that uses mDixon MR acquisition and advanced machine learning methods for synthetic CT generation. Both transfer fuzzy clustering and active learning-based classification (TFC-ALC) are used. The significance of our efforts is fourfold: 1) TFC-ALC is capable of better synthetic CT generation than methods currently in use on the challenging abdomen using only common Dixon-based scanning. 2) TFC partitions MR voxels initially into the four groups regarding fat, bone, air, and soft tissue via transfer learning; ALC can learn insightful classifiers, using as few but informative labeled examples as possible to precisely distinguish bone, air, and soft tissue. Combining them, the TFC-ALC method successfully overcomes the inherent imperfection and potential uncertainty regarding the co-registration between CT and MR images. 3) Compared with existing methods, TFC-ALC features not only preferable synthetic CT generation but also improved parameter robustness, which facilitates its clinical practicability. Applying the proposed approach on mDixon-MR data from ten subjects, the average score of the mean absolute prediction deviation (MAPD) was 89.78±8.76 which is significantly better than the 133.17±9.67 obtained using the all-water (AW) method (p=4.11E-9) and the 104.97±10.03 obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method (p=0.002). 4) Experiments in the PET SUV errors of these approaches show that TFC-ALC achieves the highest SUV accuracy and can generally reduce the SUV errors to 5% or less. These experimental results distinctively demonstrate the effectiveness of our proposed TFCALC method for the synthetic CT generation on abdomen and pelvis using only the commonly-available Dixon pulse sequence.

UTE-mDixon-based thorax synthetic CT generation.

PURPOSE: Accurate photon attenuation assessment from MR data remains an unmet challenge in the thorax due to tissue heterogeneity and the difficulty of MR lung imaging. As thoracic tissues encompass the whole physiologic range of photon absorption, large errors can occur when using, for example, a uniform, water-equivalent or a soft-tissue-only approximation. The purpose of this study was to introduce a method for voxel-wise thoracic synthetic CT (sCT) generation from MR data attenuation correction (AC) for PET/MR or for MR-only radiation treatment planning (RTP). METHODS: Acquisition: A radial stack-of-stars combining ultra-short-echo time (UTE) and modified Dixon (mDixon) sequence was optimized for thoracic imaging. The UTE-mDixon pulse sequence collects MR signals at three TE times denoted as UTE, Echo1, and Echo2. Three-point mDixon processing was used to reconstruct water and fat images. Bias field correction was applied in order to avoid artifacts caused by inhomogeneity of the MR magnetic field. ANALYSIS: Water fraction and R2* maps were estimated using the UTE-mDixon data to produce a total of seven MR features, that is UTE, Echo1, Echo2, Dixon water, Dixon fat, Water fraction, and R2*. A feature selection process was performed to determine the optimal feature combination for the proposed automatic, 6-tissue classification for sCT generation. Fuzzy c-means was used for the automatic classification which was followed by voxel-wise attenuation coefficient assignment as a weighted sum of those of the component tissues. Performance evaluation: MR data collected using the proposed pulse sequence were compared to those using a traditional two-point Dixon approach. Image quality measures, including image resolution and uniformity, were evaluated using an MR ACR phantom. Data collected from 25 normal volunteers were used to evaluate the accuracy of the proposed method compared to the template-based approach. Notably, the template approach is applicable here, that is normal volunteers, but may not be robust enough for patients with pathologies. RESULTS: The free breathing UTE-mDixon pulse sequence yielded images with quality comparable to those using the traditional breath holding mDixon sequence. Furthermore, by capturing the signal before T2* decay, the UTE-mDixon image provided lung and bone information which the mDixon image did not. The combination of Dixon water, Dixon fat, and the Water fraction was the most robust for tissue clustering and supported the classification of six tissues, that is, air, lung, fat, soft tissue, low-density bone, and dense bone, used to generate the sCT. The thoracic sCT had a mean absolute difference from the template-based (reference) CT of less than 50 HU and which was better agreement with the reference CT than the results produced using the traditional Dixon-based data. CONCLUSION: MR thoracic acquisition and analyses have been established to automatically provide six distinguishable tissue types to generate sCT for MR-based AC of PET/MR and for MR-only RTP.

Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach.

BACKGROUND: Manual contouring remains the most laborious task in radiation therapy planning and is a major barrier to implementing routine Magnetic Resonance Imaging (MRI) Guided Adaptive Radiation Therapy (MR-ART). To address this, we propose a new artificial intelligence-based, auto-contouring method for abdominal MR-ART modeled after human brain cognition for manual contouring. METHODS/MATERIALS: Our algorithm is based on two types of information flow, i.e. top-down and bottom-up. Top-down information is derived from simulation MR images. It grossly delineates the object based on its high-level information class by transferring the initial planning contours onto daily images. Bottom-up information is derived from pixel data by a supervised, self-adaptive, active learning based support vector machine. It uses low-level pixel features, such as intensity and location, to distinguish each target boundary from the background. The final result is obtained by fusing top-down and bottom-up outputs in a unified framework through artificial intelligence fusion. For evaluation, we used a dataset of four patients with locally advanced pancreatic cancer treated with MR-ART using a clinical system (MRIdian, Viewray, Oakwood Village, OH, USA). Each set included the simulation MRI and onboard T1 MRI corresponding to a randomly selected treatment session. Each MRI had 144 axial slices of 266 × 266 pixels. Using the Dice Similarity Index (DSI) and the Hausdorff Distance Index (HDI), we compared the manual and automated contours for the liver, left and right kidneys, and the spinal cord. RESULTS: The average auto-segmentation time was two minutes per set. Visually, the automatic and manual contours were similar. Fused results achieved better accuracy than either the bottom-up or top-down method alone. The DSI values were above 0.86. The spinal canal contours yielded a low HDI value. CONCLUSION: With a DSI significantly higher than the usually reported 0.7, our novel algorithm yields a high segmentation accuracy. To our knowledge, this is the first fully automated contouring approach using T1 MRI images for adaptive radiotherapy.

Comparative analysis for renal stereotactic body radiotherapy using Cyberknife, VMAT and proton therapy based treatment planning.

PURPOSE: We conducted this dosimetric analysis to evaluate the feasibility of a multi-center stereotactic body radiation therapy (SBRT) trial for renal cell carcinoma (RCC) using different SBRT platforms. MATERIALS/METHODS: The computed tomography (CT) simulation images of 10 patients with unilateral RCC previously treated on a Phase 1 trial at Institution 1 were anonymized and shared with Institution 2 after IRB approval. Treatment planning was generated through five different platforms aiming a total dose of 48 Gy in three fractions. These platforms included: Cyberknife and volumetric modulated arc therapy (VMAT) at institution 1, and Cyberknife, VMAT, and pencil beam scanning (PBS) Proton Therapy at institution 2. Dose constraints were based on the Phase 1 approved trial. RESULTS: Compared to Cyberknife, VMAT and PBS plans provided overall an equivalent or superior coverage to the target volume, while limiting dose to the remaining kidney, contralateral kidney, liver, spinal cord, and bowel. CONCLUSION: This dosimetric study supports the feasibility of a multi-center trial for renal SBRT using PBS, VMAT and Cyberknife.

Three-dimensional (3D) culture of adult murine colon as an in vitro model of cryptosporidiosis: Proof of concept.

Cryptosporidium parvum is a major cause of diarrheal illness and was recently potentially associated with digestive carcinogenesis. Despite its impact on human health, Cryptosporidium pathogenesis remains poorly known, mainly due to the lack of a long-term culture method for this parasite. Thus, the aim of the present study was to develop a three-dimensional (3D) culture model from adult murine colon allowing biological investigations of the host-parasite interactions in an in vivo-like environment and, in particular, the development of parasite-induced neoplasia. Colonic explants were cultured and preserved ex vivo for 35 days and co-culturing was performed with C. parvum. Strikingly, the resulting system allowed the reproduction of neoplastic lesions in vitro at 27 days post-infection (PI), providing new evidence of the role of the parasite in the induction of carcinogenesis. This promising model could facilitate the study of host-pathogen interactions and the investigation of the process involved in Cryptosporidium-induced cell transformation.

Outcomes and toxicities in patients treated with definitive focal therapy for primary prostate cancer: systematic review.

AIM: This systematic review summarizes the clinical data on focal therapy (FT) when used alone as definitive therapy for primary prostate cancer (PCa). METHODS: The protocol is detailed in the online PROSPERO database, registration No. CRD42014014765. Articles evaluating any form of FT alone as a definitive treatment for PCa in adult male patients were included. RESULTS: Of 10,419 identified articles, 10,401 were excluded, and thus leaving 18 for analysis. In total, 2288 patients were treated using seven modalities. The outcomes of FT in PCa seem to be similar to those observed with whole gland therapy and with fewer side effects. CONCLUSION: Further research, including prospective randomized trials, is warranted to elucidate the potential advantages of focal radiation techniques for treating PCa. Prospero Registration Number: CRD42014014765.

Digoxin therapy for cor pulmonale: A systematic review.

OBJECTIVE: Right heart failure is associated with increased mortality and morbidity. The optimal treatment for patients with RV failure is not established. The aim of this study is to conduct a systematic review of the literature to assess the relative benefits and harms of digoxin therapy in patients with RV failure. METHODS: We performed a literature search in MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) on Nov. 4, 2014. We did not use publication type, period or language restrictions to the search strategy. Exclusions included: trials that excluded patients with RV failure, included patients requiring mechanical or intravenous inotropic support, review papers and case reports. The primary outcome was long-term efficacy outcomes of digoxin in right heart failure. Two reviewers screened titles and abstracts of identified citations independently and in duplication using calibration exercises and standardized screening forms. RESULTS: The search strategy identified 4097 citations, and 4 studies were included in this analysis (n=76 patients). Of the four studies, two assessed improvements in RVEF, two studies compared exercise capacity indexes, and one assessed symptoms with digoxin compared with placebo. No study assessed mortality outcomes. Overall, there was no statistically significant improvement in RVEF, exercise capacity, NYHA class, heart failure score, or body weight. CONCLUSIONS: There are few studies evaluating Digitalis use for RV failure, which are limited to patients with cor pulmonale. In these patients, Digitalis use provides no improvement in RVEF, exercise capacity, or NYHA class. Randomized clinical trials are needed to address this question.

Saphenous vein graft aneurysm presenting as abdominal pain.

Aneurysmal dilation of a saphenous vein aortocoronary graft remains a rare complication. We report a patient with saphenous vein graft aneurysm who presented with abdominal pain due to compression of the adjacent liver 43 years after the coronary bypass operation.

Physical symptom profile for adult cancer inpatients at a Lebanese cancer unit.

BACKGROUND: Hospital-based palliative care programs in Lebanon are nonexistent in a structured form. One of obstacles is the lack of knowledge about symptom prevalence and burden of cancer patients in Lebanon. METHODS: This is a cross-sectional observational study where 100 adult cancer patients admitted to the American University of Beirut Medical Center inpatient unit completed a survey to assess 20 physical symptoms according the National Cancer Institute's Common Terminology Criteria for Adverse Events 4.0 (NCI-CTCAE 4.0) guidelines. RESULTS: Hematologic, gastrointestinal, breast, and lung cancers were the most common. Mean age was 51.5 years; 51% were female. 74% of patients with solid tumors had metastatic disease. Treatment approaches were palliative chemotherapy, followed by curative chemotherapy and best supportive care. The most common symptoms were fatigue, appetite loss, nausea, and pain; most distressing were nausea, pain, and fatigue. Nausea and vomiting were more prevalent among females than males. Females reported more severe vomiting than males, but males had more intense pain. Overall symptom burden difference was statistically significant across age groups, with the 51-60 year group having the most symptoms, but not among different genders. Difference was significant among different treatment intents, with the best supportive care group having most symptoms. CONCLUSION: Fatigue should be better addressed as a legitimate symptom. Subgroup differences must be considered when managing gastrointestinal symptoms. Pain should be more effectively managed, and vulnerable subgroups such as the 51-60 year age group and those on best supportive care should receive special consideration.

Hematological manifestations of celiac disease.

Celiac disease, an autoimmune disease once thought to be uncommon, is now being increasingly identified. Our improved diagnostic modalities have allowed us to diagnose more and more patients with atypical symptoms who improve on gluten-free diet (GFD). We discuss here the latest findings regarding the various hematological manifestations of celiac disease and their management. Anemia remains the most common hematological manifestation of celiac disease due to many mechanisms, and can be the sole presenting symptom. Other manifestations include thrombocytosis and thrombocythemia, leukopenia, thromboembolism, increased bleeding tendency, IgA deficiency, splenic dysfunction, and lymphoma. The diagnosis of celiac disease should always be kept in mind when a patient presents with unexplained and isolated hematological finding. Once diagnosed, patients should adhere to GFD and be educated about the potential complications of this disease. We herein present an algorithm for adequate management and follow-up.