The Impact of an Artificial Intelligence Certificate Program on Radiology Resident Education.

RATIONALE AND OBJECTIVES: The objective of this study was to evaluate the effectiveness of a pilot artificial intelligence (AI) certificate program in aiding radiology trainees to develop an understanding of the evolving role and application of artificial intelligence in radiology. A secondary objective was set to determine the background of residents that would most benefit from such training. MATERIALS AND METHODS: This was a prospective pilot study involving 42 radiology residents at two separate residency programs who participated in the Radiological Society of North America Imaging AI Foundational Certificate course over a four-month period. The course consisted of 6 online modules that contained didactic lectures followed by end-of-module quizzes to assess knowledge gained from these lectures. Pre- and post-course assessments were conducted to evaluate the residents' knowledge and skills in AI. Additionally, a post-course survey was performed to assess participants' overall satisfaction with the course. RESULTS: All participating residents completed the certificate program. The mean pre-course assessment score was 37 %, which increased to 73 % after completing the modules (p < 0.001). 74 % (31/42) endorsed the belief the course improved familiarity with artificial intelligence in radiology. Residency program, residency year, and reported prior familiarity with AI were not found to influence pre-course score, post-course score, nor score improvement. 57 % (24/42) endorsed interest in pursuing further certification in AI. CONCLUSION: Our pilot study suggests that a certificate course can effectively enhance the knowledge and skills of radiology residents in the application of AI in radiology. The benefits of such a course can be found regardless of program, resident year, and self-reported prior resident understanding of radiology in AI.

New Frontiers in Breast Cancer Imaging: The Rise of AI.

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.

The RSNA ImageShare - Enabling Secure, Transparent, and Easy Image Exchange.

In the early 2000s, the radiology community was awakened to the limitations of electronic media (CDs, DVDs) for exchanging imaging exams. Clinicians frustrated by the time-consuming task of opening discs, while Internet-based exchange of music, photos, and videos were becoming more widespread. The RSNA, which had extensive experience working on interoperability issues in medical imaging, began to look for opportunities to address the issue. In 2007, in the wake of the financial crisis, the National Institute of Biomedical Imaging and Bioengineering (NIBIB) issued an RFP to address Internet-based exchange of medical images. The RFP defined requirements for the network, including that it needed to be patient controlled and standards based. The RSNA was awarded funding for what came to be known as RSNA ImageShare. Over the next 8 years, the RSNA worked in partnership with several vendors and academic institutions to create a network for sharing image-enabled personal health records (PHR). The foundation of interoperability standards used in ImageShare was provided by Integrating the Healthcare Enterprise (IHE), a standards-development organization with which RSNA has had a long association. In 2018 and 2019, the RSNA looked at what had been accomplished and asked if we could take that next step at a national level and promote a solution by which any standards-compliant party could exchange imaging exams through an HIE mechanism.

Predicting Patient Demographics From Chest Radiographs With Deep Learning.

BACKGROUND: Deep learning models are increasingly informing medical decision making, for instance, in the detection of acute intracranial hemorrhage and pulmonary embolism. However, many models are trained on medical image databases that poorly represent the diversity of the patients they serve. In turn, many artificial intelligence models may not perform as well on assisting providers with important medical decisions for underrepresented populations. PURPOSE: Assessment of the ability of deep learning models to classify the self-reported gender, age, self-reported ethnicity, and insurance status of an individual patient from a given chest radiograph. METHODS: Models were trained and tested with 55,174 radiographs in the MIMIC Chest X-ray (MIMIC-CXR) database. External validation data came from two separate databases, one from CheXpert and another from a multihospital urban health care system after institutional review board approval. Macro-averaged area under the curve (AUC) values were used to evaluate performance of models. Code used for this study is open-source and available at https://github.com/ai-bias/cxr-bias, and pixelstopatients.com/models/demographics. RESULTS: Accuracy of models to predict gender was nearly perfect, with 0.999 (95% confidence interval: 0.99-0.99) AUC on held-out test data and 0.994 (0.99-0.99) and 0.997 (0.99-0.99) on external validation data. There was high accuracy to predict age and ethnicity, ranging from 0.854 (0.80-0.91) to 0.911 (0.88-0.94) AUC, and moderate accuracy to predict insurance status, with AUC ranging from 0.705 (0.60-0.81) on held-out test data to 0.675 (0.54-0.79) on external validation data. CONCLUSIONS: Deep learning models can predict the age, self-reported gender, self-reported ethnicity, and insurance status of a patient from a chest radiograph. Visualization techniques are useful to ensure deep learning models function as intended and to demonstrate anatomical regions of interest. These models can be used to ensure that training data are diverse, thereby ensuring artificial intelligence models that work on diverse populations.

Moving Toward Seamless Interinstitutional Electronic Image Transfer.

The fact that medical images are still predominately exchanged between institutions via physical media is unacceptable in the era of value-driven health care. Although better solutions are technically possible, problems of coordination and market dynamics may be inhibiting progress more than technical factors. We provide a macrosystem analysis of the problem of interinstitutional medical image exchange and propose a strategy for nudging the market toward a patient-friendly solution. The system can be viewed as a network, with autonomous nodes interconnected by links through which information is exchanged. A variety of potential network configurations include those that depend on individual carriers, peer-to-peer links, one or multiple hubs, or a hybrid of models. We find the linked multihub model, in which individual institutions are connected to other institutions via image exchange companies, to be the configuration most likely to create a patient-friendly electronic image exchange system. To achieve this configuration, image exchange companies, which operate in a competitive marketplace, must exchange images with each other. We call on these vendors to immediately commit to coordinating in this manner. We call on all other stakeholders, including local care provider institutions, medical societies, payers, and regulators, to actively encourage and facilitate this behavior. Specifically, we call on institutions to create appropriate market incentives by only contracting with image exchange vendors who are committed to begin vendor-to-vendor image exchange by no later than 2024.

An Interview with the Carequality Imaging Use Case Implementers.

Imagine you had a cell phone plan that only allowed you to call other customers within the same carrier network. That is the situation most healthcare providers experience when joining a data sharing network. Carequality is a network-to-network trust framework that brings together the entire healthcare industry to overcome this challenge by providing a national-level, consensus built, common interoperability framework to enable health information exchange between and among health data sharing networks. The RSNA partnered with Carequality in 2019 to develop an implementation guide to enable the Imaging Exchange Use Case. The implementation guide was published in December 2019 for early adopters to sign up as implementers to the Carequality framework. Exchange standards must be clearly laid out so that all implementers can easily follow and be held accountable to enable interoperability of medical imaging. The guide was reviewed and tested by implementers and approved for production use in March 2021. Since the launch of the implementation guide, five Carequality Implementers have participated in Carequality's Image Exchange Use Case: Ambra Health, Hyland, Life Image, Nuance, and Philips. These implementers recognized a gap in image interoperability and the need for change and collaboration. Carequality has asked each of the implementers to share their thoughts on issues pertinent to becoming an implementer and imaging interoperability with the hope that the reader will gain insight as to the evolution of network-based image exchange.

Initial Management of Noncastrate Advanced, Recurrent, or Metastatic Prostate Cancer: ASCO Guideline Update.

PURPOSE: Update all preceding ASCO guidelines on initial hormonal management of noncastrate advanced, recurrent, or metastatic prostate cancer. METHODS: The Expert Panel based recommendations on a systematic literature review. Recommendations were approved by the Expert Panel and the ASCO Clinical Practice Guidelines Committee. RESULTS: Four clinical practice guidelines, one clinical practice guidelines endorsement, 19 systematic reviews with or without meta-analyses, 47 phase III randomized controlled trials, nine cohort studies, and two review papers informed the guideline update. RECOMMENDATIONS: Docetaxel, abiraterone, enzalutamide, or apalutamide, each when administered with androgen deprivation therapy (ADT), represent four separate standards of care for noncastrate metastatic prostate cancer. Currently, the use of any of these agents in any particular combination or series cannot be recommended. ADT plus docetaxel, abiraterone, enzalutamide, or apalutamide should be offered to men with metastatic noncastrate prostate cancer, including those who received prior therapies, but have not yet progressed. The combination of ADT plus abiraterone and prednisolone should be considered for men with noncastrate locally advanced nonmetastatic prostate cancer who have undergone radiotherapy, rather than castration monotherapy. Immediate ADT may be offered to men who initially present with noncastrate locally advanced nonmetastatic disease who have not undergone previous local treatment and are unwilling or unable to undergo radiotherapy. Intermittent ADT may be offered to men with high-risk biochemically recurrent nonmetastatic prostate cancer. Active surveillance may be offered to men with low-risk biochemically recurrent nonmetastatic prostate cancer. The panel does not support use of either micronized abiraterone acetate or the 250 mg dose of abiraterone with a low-fat breakfast in the noncastrate setting at this time.Additional information is available at www.asco.org/genitourinary-cancer-guidelines.

Diaphragmatic excursion: Quantitative measure to assess adequacy of expiratory phase CT chest images.

OBJECTIVE: To evaluate diaphragmatic excursion as a quantitative metric for change in lung volume between inspiratory and expiratory chest computed tomography (CT) images. METHODS: A 12-month retrospective review identified 226 chest CT exams with inspiratory and expiratory phase imaging, 63 in individuals referred with diagnosis of asthma by ICD9/10 code. Exams acquired in the supine position at 1.25 mm slice thickness in each phase were included (n = 30, mean age = 62, M = 15, F = 15). Diaphragmatic excursion was calculated as the difference between axial slices through the lungs on inspiration and expiration, using the lung apex as the cranial bound, and the hemidiaphragm caudally. Inspiratory and expiratory lung and tracheal volumes were calculated through volumetric segmentation. Tracheal morphology was assessed at 1 cm above the level of the aortic arch, and 1 cm above the carina. RESULTS: Inspiratory and expiratory lung volumes were higher in men (mean I = 5 + 1.6 L, E = 3.1 + 1.2 L) than women (mean I = 3.6 + 0.8 L, E = 2.4 + 0.7 L), p = .005 and p = .047, respectively. Average inspiratory and expiratory tracheal volumes were higher in men (I = 61 + 17 mL, E = 43 + 14) than women (I = 44 + 14, E = 30 + 8), p = .006 and p = .005. Average change in lung and tracheal volume between inspiratory and expiratory scans did not significantly differ between men and women. Average diaphragmatic excursion was 2.5 cm between inspiratory and expiratory scans (2.7 cm in men, 2.3 cm in women; p = .5). There was a strong positive correlation between diaphragmatic excursion and change in lung (r = .84) and tracheal volume (r = .79). A moderate correlation was also found between change in tracheal volume and change in lung volume (r = 0.67). Change in tracheal morphology between inspiratory and expiratory imaging was associated with change in tracheal volume at both 1 cm above the aortic arch (p = .04) and 1 cm above the carina (p = .008); there was no association with diaphragmatic excursion or lung volume. CONCLUSIONS: Diaphragmatic excursion is a quantitative measure of expiratory effort as validated by both lung and tracheal volumes in asthma patients, and may be more accurate than qualitative assessment based on tracheal morphology.

Interoperability and Considerations for Standards-Based Exchange of Medical Images: HIMSS-SIIM Collaborative White Paper.

This white paper explores the considerations of standards-based interoperability of medical images between organizations, patients, and providers. In this paper, we will look at three different standards-based image exchange implementations that have been deployed to facilitate exchange of images between provider organizations. The paper will describe how each implementation uses applicable technology and standards; the image types that are included; and the governance policies that define participation, access, and trust. Limitations of the solution or non-standard approaches to solve challenges will also be identified. Much can be learned from successes elsewhere, and those learnings will point to recommendations of best practices to facilitate the adoption of image exchange.

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.

Effect of Clinical Decision Support on Appropriateness of Advanced Imaging Use Among Physicians-in-Training.

OBJECTIVE: Clinical decision support (CDS) tools have been shown to reduce inappropriate imaging orders. We hypothesized that CDS may be especially effective for house staff physicians who are prone to overuse of resources. MATERIALS AND METHODS: Our hospital implemented CDS for CT and MRI orders in the emergency department with scores based on the American College of Radiology's Appropriateness Criteria (range, 1-9; higher scores represent more-appropriate orders). Data on CT and MRI orders from April 2013 through June 2016 were categorized as pre-CDS or baseline, post-CDS period 1 (i.e., intervention with active feedback for scores of ≤ 4), and post-CDS period 2 (i.e., intervention with active feedback for scores of ≤ 6). Segmented regression analysis with interrupted time series data estimated changes in scores stratified by house staff and non-house staff. Generalized linear models further estimated the modifying effect of the house staff variable. RESULTS: Mean scores were 6.2, 6.2, and 6.7 in the pre-CDS, post-CDS 1, and post-CDS 2 periods, respectively (p < 0.05). In the segmented regression analysis, mean scores significantly (p < 0.05) increased when comparing pre-CDS versus post-CDS 2 periods for both house staff (baseline increase, 0.41; 95% CI, 0.17-0.64) and non-house staff (baseline increase, 0.58; 95% CI, 0.34-0.81), showing no differences in effect between the cohorts. The generalized linear model showed significantly higher scores, particularly in the post-CDS 2 period compared with the pre-CDS period (0.44 increase in scores; p < 0.05). The house staff variable did not significantly change estimates in the post-CDS 2 period. CONCLUSION: Implementation of active CDS increased overall scores of CT and MRI orders. However, there was no significant difference in effect on scores between house staff and non-house staff.

Causative factors, imaging findings, and CT course of round atelectasis.

OBJECTIVE: To assess causative factors, associated imaging findings, and CT course of round atelectasis (RA). MATERIALS AND METHODS: We retrospectively reviewed CT reports for "round" or "rounded atelectasis" over a 5-year time frame. Patients with at least 2 CT scans a minimum of 6 months apart were included. Electronic medical records and clinical and imaging follow-up was reviewed for all cases. RESULTS: Study population included 50 individuals with mean age of 63 years, and 59 unique instances of RA. The most commonly associated etiologies were hepatic hydrothorax (26%, n = 13) and asbestos exposure (26%), followed by post-infectious pleural inflammation (22%), congestive heart failure (12%), and end stage renal disease (8%). RA was found in the right lower lobe in over half of cases (n = 30). Association with one or more pleural abnormality was identified in all cases, including thickening (88%), fluid (60%), or calcification (40%). Nearly one third (n = 19) demonstrated intra-lesional calcification. In those who underwent PET/CT (20%), lesions demonstrated an average SUV of 2.2 (range 0-7.8). CT course over mean follow up of 32 months (range 6-126 months), demonstrated RA to remain stable (n = 26) or decrease (n = 26) in size in the majority (88%) of cases. CONCLUSION: Round atelectasis may arise from diverse etiologies beyond asbestos, and will most often decrease or remain stable in size over serial exams. Accurate identification may obviate the need for added diagnostic interventions.

A first-in-human dose-escalation study of the oral proteasome inhibitor oprozomib in patients with advanced solid tumors.

PURPOSE: To determine the dose-limiting toxicities (DLTs), maximum tolerated dose (MTD), safety, and pharmacokinetic and pharmacodynamic profiles of the tripeptide epoxyketone proteasome inhibitor oprozomib in patients with advanced refractory or recurrent solid tumors. METHODS: Patients received escalating once daily (QD) or split doses of oprozomib on days 1-5 of 14-day cycles (C). The split-dose arm was implemented and compared in fasted (C1) and fed (C2) states. Pharmacokinetic samples were collected during C1 and C2. Proteasome inhibition was evaluated in red blood cells and peripheral blood mononuclear cells. RESULTS: Forty-four patients (QD, n = 25; split dose, n = 19) were enrolled. The most common primary tumor types were non-small cell lung cancer (18%) and colorectal cancer (16%). In the 180-mg QD cohort, two patients experienced DLTs: grade 3 vomiting and dehydration; grade 3 hypophosphatemia (n = 1 each). In the split-dose group, three DLTs were observed (180-mg cohort: grade 3 hypophosphatemia; 210-mg cohort: grade 5 gastrointestinal hemorrhage and grade 3 hallucinations (n = 1 each). In the QD and split-dose groups, the MTD was 150 and 180 mg, respectively. Common adverse events (all grades) included nausea (91%), vomiting (86%), and diarrhea (61%). Peak concentrations and total exposure of oprozomib generally increased with the increasing dose. Oprozomib induced dose-dependent proteasome inhibition. Best response was stable disease. CONCLUSIONS: While generally low-grade, clinically relevant gastrointestinal toxicities occurred frequently with this oprozomib formulation. Despite dose-dependent increases in pharmacokinetics and pharmacodynamics, single-agent oprozomib had minimal antitumor activity in this patient population with advanced solid tumors.

Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining.

OBJECTIVE: The purpose of this article is to describe structured reporting and the development of large databases for use in data mining in breast imaging. CONCLUSION: The results of millions of breast imaging examinations are reported with structured tools based on the BI-RADS lexicon. Much of these data are stored in accessible media. Robust computing power creates great opportunity for data scientists and breast imagers to collaborate to improve breast cancer detection and optimize screening algorithms. Data mining can create knowledge, but the questions asked and their complexity require extremely powerful and agile databases. New data technologies can facilitate outcomes research and precision medicine.

Patient-directed Internet-based Medical Image Exchange: Experience from an Initial Multicenter Implementation.

RATIONALE AND OBJECTIVES: Inefficient transfer of personal health records among providers negatively impacts quality of health care and increases cost. This multicenter study evaluates the implementation of the first Internet-based image-sharing system that gives patients ownership and control of their imaging exams, including assessment of patient satisfaction. MATERIALS AND METHODS: Patients receiving any medical imaging exams in four academic centers were eligible to have images uploaded into an online, Internet-based personal health record. Satisfaction surveys were provided during recruitment with questions on ease of use, privacy and security, and timeliness of access to images. Responses were rated on a five-point scale and compared using logistic regression and McNemar's test. RESULTS: A total of 2562 patients enrolled from July 2012 to August 2013. The median number of imaging exams uploaded per patient was 5. Most commonly, exams were plain X-rays (34.7%), computed tomography (25.7%), and magnetic resonance imaging (16.1%). Of 502 (19.6%) patient surveys returned, 448 indicated the method of image sharing (Internet, compact discs [CDs], both, other). Nearly all patients (96.5%) responded favorably to having direct access to images, and 78% reported viewing their medical images independently. There was no difference between Internet and CD users in satisfaction with privacy and security and timeliness of access to medical images. A greater percentage of Internet users compared to CD users reported access without difficulty (88.3% vs. 77.5%, P < 0.0001). CONCLUSION: A patient-directed, interoperable, Internet-based image-sharing system is feasible and surpasses the use of CDs with respect to accessibility of imaging exams while generating similar satisfaction with respect to privacy.

Pharmacodynamics and pharmacokinetics of oral topotecan in patients with advanced solid tumours and impaired renal function.

AIMS: The aim of the study was to determine the effect of renal impairment and prior platinum-based chemotherapy on the toxicity and pharmacokinetics of oral topotecan and to identify recommended doses for patients with renal impairment or prior platinum-based (PB) chemotherapy. METHODS: A multicentre phase I toxicity and pharmacokinetic study of oral topotecan was conducted in patients with advanced solid tumours. Patients were grouped by normal renal function with limited or prior PB chemotherapy or impaired renal function (mild [creatinine clearance (CLcr) = 50-79 ml min(-1) ], moderate [CLcr = 30-49 ml min(-1) ], severe [CLcr <30 ml min(-1) ]). RESULTS: Fifty-nine patients were evaluable. Topotecan lactone and total topotecan area under the concentration-time curve (AUC) was significantly increased in patients with moderate and severe renal impairment (109% and 174%, respectively, topotecan lactone and 148% and 298%, respectively, total topotecan). Asian patients (23 in total) had higher AUCs than non-Asian patients with the same degree of renal impairment. Thirteen dose-limiting toxicities (DLTs) were observed, which were mostly haematological. The maximum tolerated dose (MTD) was 2.3 mg m(-2) day(-1) , given on days 1 to 5 in a 21 day cycle, for patients with prior PB chemotherapy or mild renal impairment, and 1.2 mg m(-2) day(-1) for patients with moderate renal impairment (suggested dose 1.9 mg m(-2) day(-1) for non-Asians). Due to incomplete enrolment of patients with severe renal impairment, the MTD was determined as ≥ 0.6 mg m(-2) day(-1) in this cohort. CONCLUSIONS: Oral topotecan dose adjustments are not required in patients with prior PB chemotherapy or mildly impaired renal function, but reduced doses are required for patients with moderate or severe renal impairment.

Image sharing: evolving solutions in the age of interoperability.

Interoperability is a major focus of the quickly evolving world of Health IT. Easy, yet secure and confidential exchange of imaging exams and the associated reports must be a part of the solutions that are implemented. The availability of historical exams is essential in providing a quality interpretation and reducing inappropriate utilization of imaging services. Today, the exchange of imaging exams is most often achieved via a compact disc. We describe the virtues of this solution as well as challenges that have surfaced. Internet- and cloud-based technologies employed for many consumer services can provide a better solution. Vendors are making these solutions available. Standards for Internet-based exchange are emerging. Just as radiology converged on DICOM as a standard to store and view images, we need a common exchange standard. We will review the existing standards and how they are organized into useful workflows through Integrating the Healthcare Enterprise profiles. Integrating the Healthcare Enterprise and standards development processes are discussed. Health care and the domain of radiology must stay current with quickly evolving Internet standards. The successful use of the "cloud" will depend on both the technologies and the policies put into place around them, both of which we discuss. The radiology community must lead the way and provide a solution that works for radiologists and clinicians with use of the electronic medical record. We describe features we believe radiologists should consider when adding Internet-based exchange solutions to their practice.

Adjuvant and salvage radiotherapy after prostatectomy: American Society of Clinical Oncology clinical practice guideline endorsement.

PURPOSE: To endorse the American Urological Association (AUA)/American Society for Radiation Oncology (ASTRO) guideline on adjuvant and salvage radiotherapy after prostatectomy. The American Society of Clinical Oncology (ASCO) has a policy and set of procedures for endorsing clinical practice guidelines developed by other professional organizations. METHODS: The guideline on adjuvant and salvage radiotherapy after prostatectomy was reviewed for developmental rigor by methodologists. An ASCO endorsement panel then reviewed the content and recommendations. RESULTS: The panel determined that the guideline recommendations on adjuvant and salvage radiotherapy after prostatectomy, published in August 2013, are clear, thorough, and based on the most relevant scientific evidence. ASCO endorsed the guideline on adjuvant and salvage radiotherapy after prostatectomy, adding one qualifying statement that not all candidates for adjuvant or salvage radiotherapy have the same risk of recurrence or disease progression, and thus, risk-benefit ratios are not the same for all men. Those at the highest risk for recurrence after radical prostatectomy include men with seminal vesicle invasion, Gleason score 8 to 10, extensive positive margins, and detectable postoperative prostate-specific antigen (PSA). RECOMMENDATIONS: Physicians should discuss adjuvant radiotherapy with patients with adverse pathologic findings at prostatectomy (ie, seminal vesicle invasion, positive surgical margins, extraprostatic extension) and salvage radiotherapy with patients with PSA or local recurrence after prostatectomy. The discussion of radiotherapy should include possible short- and long-term adverse effects and potential benefits. The decision to administer radiotherapy should be made by the patient and multidisciplinary treatment team, keeping in mind that not all men are at equal risk of recurrence or clinically meaningful disease progression. Thus, the risk-benefit ratio will differ for each patient.

An open-label phase Ib dose-escalation study of TRC105 (anti-endoglin antibody) with bevacizumab in patients with advanced cancer.

PURPOSE: Endoglin, an endothelial cell membrane receptor expressed on angiogenic tumor vessels, is essential for angiogenesis and upregulated in the setting of VEGF inhibition. TRC105 is an anti-endoglin IgG1 monoclonal antibody that potentiates VEGF inhibitors in preclinical models. This study assessed safety, pharmacokinetics, and antitumor activity of TRC105 in combination with bevacizumab. EXPERIMENTAL DESIGN: Patients (n = 38) with advanced solid tumors, Eastern Cooperative Group performance status 0-1, and normal organ function were treated with escalating doses of TRC105 plus bevacizumab until disease progression or unacceptable toxicity using a standard 3 + 3 phase I design. RESULTS: TRC105 and bevacizumab were well tolerated at their recommended single-agent doses (10 mg/kg) when the initial dose of TRC105 was delayed by one week and divided over 2 days to limit the frequency of headache. The concurrent administration of bevacizumab and TRC105 did not otherwise potentiate known toxicities of TRC105 or bevacizumab. Hypertension and proteinuria were observed, though not at rates expected for single-agent bevacizumab. Several patients who had previously progressed on bevacizumab or VEGF receptor tyrosine kinase inhibitor (VEGFR TKI) treatment experienced reductions in tumor volume, including two partial responses by RECIST, and 6 remained without progression for longer periods than during their prior VEGF inhibitor therapy. CONCLUSIONS: TRC105 was well tolerated with bevacizumab and clinical activity was observed in a VEGF inhibitor-refractory population. Ongoing clinical trials are testing TRC105 in combination with bevacizumab in glioblastoma and with VEGFR TKIs in renal cell carcinoma, hepatocellular carcinoma, and soft tissue sarcoma.