Predicting the 3-Dimensional Dose Distribution of Multi-Lesion Lung Stereotactic Ablative Radiotherapy with Generative Adversarial Networks.

PURPOSE: As stereotactic ablative radiotherapy (SABR) is being used to treat greater numbers of lung metastases, selecting the optimal dose and fractionation to balance local failure and treatment toxicity becomes increasingly challenging. Multi-lesion lung SABR plans include spatially diverse lesions with heterogenous prescriptions and interacting dose distributions. In this study, we developed and evaluated a generative adversarial network (GAN) to provide real-time dosimetry predictions for these complex cases. METHODS AND MATERIALS: A GAN was trained to predict dosimetry on a dataset of patients who received SABR for lung lesions at a tertiary center. Model input included the planning CT scan, the organs at risk and (OARs) target structures, and an initial estimate of exponential dose fall-off. Multi-lesion plans were split 80/20 for training and evaluation. Models were evaluated on voxel-voxel, clinical dose-volume-histogram, and conformality metrics. An out-of-sample validation and analysis of model variance were performed. RESULTS: There were 125 multi-lesion plans from 102 patients with 357 lesions. Patients were treated to 2-7 lesions, with 19 unique dose-fractionation schemes over 1-3 courses of treatment. The out-of-sample validation set contained an additional 90 plans from 80 patients. The mean absolute difference (MAD) and gamma pass fraction (GPF) between the predicted and true dosimetry was <3 Gy and > 90% for all OARs. The absolute differences in lung V20 and CV14 were 1.40±0.99% and 75.8±42.0 cc respectively. The ratios of predicted to true R50%, R100% and D2cm were 1.00±0.16, 0.96±0.32, and 1.01±0.36 respectively. The out-of-sample validation set maintained MAD and GPF of <3 Gy and >90% for all OARs. The median standard deviation of variance in V20 and CV14 prediction was 0.49% and 22.2 cc respectively. CONCLUSIONS: A GAN for predicting the 3-D dosimetry of complex multi-lesion lung SABR is presented. Rapid dosimetry prediction can be used to assess treatment feasibility and explore dosimetric differences between varying prescriptions.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever­growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi­society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety.• Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance.• AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA.

Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. KEY POINTS.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Self-supervised enhanced thyroid nodule detection in ultrasound examination video sequences with multi-perspective evaluation.

Objective.Ultrasound is the most commonly used examination for the detection and identification of thyroid nodules. Since manual detection is time-consuming and subjective, attempts to introduce machine learning into this process are ongoing. However, the performance of these methods is limited by the low signal-to-noise ratio and tissue contrast of ultrasound images. To address these challenges, we extend thyroid nodule detection from image-based to video-based using the temporal context information in ultrasound videos.Approach.We propose a video-based deep learning model with adjacent frame perception (AFP) for accurate and real-time thyroid nodule detection. Compared to image-based methods, AFP can aggregate semantically similar contextual features in the video. Furthermore, considering the cost of medical image annotation for video-based models, a patch scale self-supervised model (PASS) is proposed. PASS is trained on unlabeled datasets to improve the performance of the AFP model without additional labelling costs.Main results.The PASS model is trained by 92 videos containing 23 773 frames, of which 60 annotated videos containing 16 694 frames were used to train and evaluate the AFP model. The evaluation is performed from the video, frame, nodule, and localization perspectives. In the evaluation of the localization perspective, we used the average precision metric with the intersection-over-union threshold set to 50% (AP@50), which is the area under the smoothed Precision-Recall curve. Our proposed AFP improved AP@50 from 0.256 to 0.390, while the PASS-enhanced AFP further improved the AP@50 to 0.425. AFP and PASS also improve the performance in the valuations of other perspectives based on the localization results.Significance.Our video-based model can mitigate the effects of low signal-to-noise ratio and tissue contrast in ultrasound images and enable the accurate detection of thyroid nodules in real-time. The evaluation from multiple perspectives of the ablation experiments demonstrates the effectiveness of our proposed AFP and PASS models.

Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework.

Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and performance is improving rapidly with new applications for given use cases being developed continuously, oftentimes with development and validation occurring in parallel. Several guidelines have provided reporting standards for publications of AI-based research in medicine and radiology. Yet, there is an unmet need for recommendations on the assessment of AI software before adoption and after commercialization. As the radiology AI ecosystem continues to grow and mature, a formalization of system assessment and evaluation is paramount to ensure patient safety, relevance and support to clinical workflows, and optimal allocation of limited AI development and validation resources before broader implementation into clinical practice. To fulfil these needs, we provide a glossary for AI software types, use cases and roles within the clinical workflow; list healthcare needs, key performance indicators and required information about software prior to assessment; and lay out examples of software performance metrics per software category. This conceptual framework is intended to streamline communication with the AI software industry and provide healthcare decision makers and radiologists with tools to assess the potential use of these software. The proposed software evaluation framework lays the foundation for a radiologist-led prospective validation network of radiology AI software. Learning Points: The rapid expansion of AI applications in radiology requires standardization of AI software specification, classification, and evaluation. The Canadian Association of Radiologists' AI Tech & Apps Working Group Proposes an AI Specification document format and supports the implementation of a clinical expert evaluation process for Radiology AI software.

CT features associated with underlying malignancy in patients with diagnosed mesenteric panniculitis.

PURPOSE: The purpose of this study was to identify abdominal computed tomography (CT) features associated with underlying malignancy in patients with mesenteric panniculitis (MP). MATERIALS AND METHODS: This single-institution retrospective longitudinal cohort study included patients with MP and a minimum 1-year abdominopelvic CT follow-up or 2-year clinical follow-up after initial abdominopelvic CT examination. Two radiologists, blinded to patients' medical records, conjointly reviewed CT-based features of MP. Electronic medical records were reviewed for newly diagnosed malignancies with the following specific details: type (lymphoproliferative disease or solid malignancy), location (possible mesenteric drainage or distant), stage, time to diagnosis. An expert panel of three radiologists and one hemato-oncologist, who were blinded to the initial CT-based MP features, assessed the probability of association between MP and malignancy based on the malignancy characteristics. RESULTS: From 2006 to 2016, 444 patients with MP were included. There were 272 men and 172 women, with a median age of 64 years (age range: 25-89); the median overall follow-up was 36 months (IQR: 22, 60; range: 12-170). A total of 34 (8%) patients had a diagnosis of a new malignancy; 5 (1%) were considered possibly related to the MP, all being low-grade B-cell non-Hodgkin lymphomas. CT features associated with the presence of an underlying malignancy were the presence of an MP soft-tissue nodule with a short axis >10 mm (P < 0.0001) or lymphadenopathy in another abdominopelvic region (P < 0.0001). Associating these two features resulted in high diagnostic performance (sensitivity 100%; [95% CI: 57-100]; specificity 99% [95% CI: 98-100]). All related malignancies were identified. CONCLUSION: Further workup to rule out an underlying malignancy is only necessary in the presence of an MP soft-tissue nodule >10 mm or associated abdominopelvic lymphadenopathy.

Incidence of infectious complications following ultrasound-guided percutaneous musculoskeletal interventions with the use of an uncovered transducer footprint.

OBJECTIVES: To determine the incidence of infectious complications following ultrasound-guided musculoskeletal interventions performed with a disinfected uncovered ultrasound transducer footprint. METHODS: Electronic medical records of all patients who underwent an ultrasound-guided musculoskeletal procedure (including injection, calcific lavage, or ganglion cyst aspiration) performed by any of the 14 interventional musculoskeletal radiologists at our institution between January 2013 and December 2018 were retrospectively reviewed to identify procedure site infections. Biopsies and joint aspirations were excluded. The procedures were performed using a disinfected uncovered transducer footprint. First, an automated chart review identified cases with (1) positive answers to the nurse's post-procedure call, (2) an International Classification of Diseases (ICD) diagnostic code related to a musculoskeletal infection, or (3) an antibiotic prescription within 30 days post-procedure. Then, these cases were manually reviewed for evidence of procedure site infection. RESULTS: In total, 6511 procedures were included. The automated chart review identified 3 procedures (2 patients) in which post-procedural fever was reported during the nurse's post-procedure call, 33 procedures (28 patients) with an ICD code for a musculoskeletal infection, and 220 procedures (216 patients) with an antibiotic prescription within 30 post-procedural days. The manual chart review of these patients revealed no cases of confirmed infection and 1 case (0.015%) of possible site infection. CONCLUSIONS: The incidence of infectious complications after an ultrasound-guided musculoskeletal procedure performed with an uncovered transducer footprint is extremely low. This information allows radiologists to counsel their patients more precisely when obtaining informed consent. KEY POINTS: • Infectious complications after ultrasound-guided musculoskeletal procedures performed with a disinfected uncovered transducer footprint are extremely rare.

Impact of sarcopenia on outcomes of patients treated with trimodal therapy for muscle invasive bladder cancer.

OBJECTIVE: We sought to investigate the incidence of sarcopenia and its impact on main oncological outcomes in patients with muscle invasive bladder cancer (MIBC) treated with trimodal therapy (TMT). PATIENTS AND METHODS: This was a retrospective analysis of 141 MIBC patients treated with TMT in the period 2002 to 2018. Sarcopenia was identified through pretreatment computed tomography scans and defined as a skeletal muscle index of <55 cm2/m2 for men and <39 cm2/m2 for women. Body mass index (BMI)-adjusted definition of sarcopenia was used to evaluate for sarcopenic obesity. Uni- and multivariable analyses were performed to assess the impact of sarcopenia on initial complete response and overall survival (OS) to TMT. RESULTS: Median age at diagnosis was 73 years [range: 65-81] and median follow up was 32 months (Inter Quartile Range: 18-66). Median OS was 67 months (95% CI: 53-83). The incidence of sarcopenia and BMI-adjusted sarcopenia was 56.7% and 40.4%, respectively. On multivariable analysis, Eastern Cooperative Oncology Group performance status (HR = 2.37, 95% CI: 2.1-5.67, P = 0.001) and complete response to treatment (HR = 0.26, 95% CI: 0.14-0.049, P = 0.001] were independently associated with improved OS. Sarcopenia and BMI-adjusted sarcopenia were not independently associated with either complete response to TMT or OS. Similarly, in a subpopulation of 74 patients considered fit for radical cystectomy, we found that neither sarcopenia (P = 0.49) nor BMI-adjusted sarcopenia (P = 0.22) had an impact on OS. CONCLUSION: Sarcopenia and BMI-adjusted sarcopenia are prevalent in patients with MIBC undergoing TMT. TMT is a suitable treatment modality for patients with MIBC irrespective of their sarcopenia status.

Weakly-Supervised teacher-Student network for liver tumor segmentation from non-enhanced images.

Accurate liver tumor segmentation without contrast agents (non-enhanced images) avoids the contrast-agent-associated time-consuming and high risk, which offers radiologists quick and safe assistance to diagnose and treat the liver tumor. However, without contrast agents enhancing, the tumor in liver images presents low contrast and even invisible to naked eyes. Thus the liver tumor segmentation from non-enhanced images is quite challenging. We propose a Weakly-Supervised Teacher-Student network (WSTS) to address the liver tumor segmentation in non-enhanced images by leveraging additional box-level-labeled data (labeled with a tumor bounding-box). WSTS deploys a weakly-supervised teacher-student framework (TCH-ST), namely, a Teacher Module learns to detect and segment the tumor in enhanced images during training, which facilitates a Student Module to detect and segment the tumor in non-enhanced images independently during testing. To detect the tumor accurately, the WSTS proposes a Dual-strategy DRL (DDRL), which develops two tumor detection strategies by creatively introducing a relative-entropy bias in the DRL. To accurately predict a tumor mask for the box-level-labeled enhanced image and thus improve tumor segmentation in non-enhanced images, the WSTS proposes an Uncertainty-Sifting Self-Ensembling (USSE). The USSE exploits the weakly-labeled data with self-ensembling and evaluates the prediction reliability with a newly-designed Multi-scale Uncertainty-estimation. WSTS is validated with a 2D MRI dataset, where the experiment achieves 83.11% of Dice and 85.12% of Recall in 50 patient testing data after training by 200 patient data (half amount data is box-level-labeled). Such a great result illustrates the competence of WSTS to segment the liver tumor from non-enhanced images. Thus, WSTS has excellent potential to assist radiologists by liver tumor segmentation without contrast-agents.

Synthesis of gadolinium-enhanced liver tumors on nonenhanced liver MR images using pixel-level graph reinforcement learning.

If successful, synthesis of gadolinium (Gd)-enhanced liver tumors on nonenhanced liver MR images will be critical for liver tumor diagnosis and treatment. This synthesis will offer a safe, efficient, and low-cost clinical alternative to eliminate the use of contrast agents in the current clinical workflow and significantly benefit global healthcare systems. In this study, we propose a novel pixel-level graph reinforcement learning method (Pix-GRL). This method directly takes regular nonenhanced liver images as input and outputs AI-enhanced liver tumor images, thereby making them comparable to traditional Gd-enhanced liver tumor images. In Pix-GRL, each pixel has a pixel-level agent, and the agent explores the pixels features and outputs a pixel-level action to iteratively change the pixel value, ultimately generating AI-enhanced liver tumor images. Most importantly, Pix-GRL creatively embeds a graph convolution to represent all the pixel-level agents. A graph convolution is deployed to the agent for feature exploration to improve the effectiveness through the aggregation of long-range contextual features, as well as outputting the action to enhance the efficiency through shared parameter training between agents. Moreover, in our Pix-GRL method, a novel reward is used to measure pixel-level action to significantly improve the performance by considering the improvement in each action in each pixel with its own future state, as well as those of neighboring pixels. Pix-GRL significantly upgrades the existing medical DRL methods from a single agent to multiple pixel-level agents, becoming the first DRL method for medical image synthesis. Comprehensive experiments on three types of liver tumor datasets (benign, cancerous, and healthy controls) with 325 patients (24,375 images) show that our novel Pix-GRL method outperforms existing medical image synthesis learning methods. It achieved an SSIM of 0.85 ± 0.06 and a Pearson correlation coefficient of 0.92 in terms of the tumor size. These results prove that the potential exists to develop a successful clinical alternative to Gd-enhanced liver MR imaging.

Three-Dimensional (3D) Animation and Calculation for the Assessment of Engaging Hill-Sachs Lesions With Computed Tomography 3D Reconstruction.

PURPOSE: To dynamically assess for Hill-Sachs engagement with animated 3-dimensional (3D) shoulder models. METHODS: We created 3D shoulder models from reconstructed computed tomography (CT) images from a consecutive series of patients with recurrent anterior dislocation. They were divided into 2 groups based on the perceived Hill-Sachs severity. For our cohort of 14 patients with recurrent anterior dislocation, 4 patients had undergone osteoarticular allografting of Hill-Sachs lesions and 10 control patients had undergone CT scanning to quantify bone loss but no treatment for bony pathology. A biomechanical analysis was performed to rotate each 3D model using local coordinate systems to the classical vulnerable position of the shoulder (abduction = 90°, external rotation = 0-135°) and through a functional range. A Hill-Sachs lesion was considered "dynamically" engaging if the angle between the lesion's long axis and anterior glenoid was parallel. Results: In the vulnerable position of the shoulder, none of the Hill-Sachs lesions aligned with the anterior glenoid in any of our patients. However, in our simulated physiological shoulder range, all allograft patients and 70% of controls had positions producing alignment. CONCLUSIONS: The technique offers a visual representation of an engaging Hill-Sachs using 3D-animated reconstructions with open-source software and CT images. In our series of patients, we found multiple shoulder positions that align the Hill-Sachs and glenoid axes that do not necessarily meet the traditional definition of engagement. Identifying all shoulder positions at risk of "engaging," in a broader physiological range, may have critical implications toward selecting the appropriate surgical management of bony defects. LEVEL OF EVIDENCE: level III, case-control study.