Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

All you need is data preparation: A systematic review of image harmonization techniques in Multi-center/device studies for medical support systems.

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) models trained on multi-centric and multi-device studies can provide more robust insights and research findings compared to single-center studies. However, variability in acquisition protocols and equipment can introduce inconsistencies that hamper the effective pooling of multi-source datasets. This systematic review evaluates strategies for image harmonization, which standardizes appearances to enable reliable AI analysis of multi-source medical imaging. METHODS: A literature search using PRISMA guidelines was conducted to identify relevant papers published between 2013 and 2023 analyzing multi-centric and multi-device medical imaging studies that utilized image harmonization approaches. RESULTS: Common image harmonization techniques included grayscale normalization (improving classification accuracy by up to 24.42 %), resampling (increasing the percentage of robust radiomics features from 59.5 % to 89.25 %), and color normalization (enhancing AUC by up to 0.25 in external test sets). Initially, mathematical and statistical methods dominated, but machine and deep learning adoption has risen recently. Color imaging modalities like digital pathology and dermatology have remained prominent application areas, though harmonization efforts have expanded to diverse fields including radiology, nuclear medicine, and ultrasound imaging. In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores. CONCLUSIONS: Continued progress in image harmonization represents a promising strategy for advancing healthcare by enabling large-scale, reliable analysis of integrated multi-source datasets using AI. Standardizing imaging data across clinical settings can help realize personalized, evidence-based care supported by data-driven technologies while mitigating biases associated with specific populations or acquisition protocols.

High Dynamic Range Dual-Modal White Light Imaging Improves the Accuracy of Tumor Bed Sampling After Neoadjuvant Therapy for Breast Cancer.

OBJECTIVES: Accurate evaluation of residual cancer burden remains challenging because of the lack of appropriate techniques for tumor bed sampling. This study evaluated the application of a white light imaging system to help pathologists differentiate the components and location of tumor bed in specimens. METHODS: The high dynamic range dual-mode white light imaging (HDR-DWI) system was developed to capture antiglare reflection and multiexposure HDR transmission images. It was tested in 60 specimens of modified radical mastectomy after neoadjuvant therapy. We observed the differential transmittance among tumor tissue, fibrosis tissue, and adipose tissue. RESULTS: The sensitivity and specificity of HDR-DWI were compared with x-ray or visual examination to determine whether HDR-DWI was superior in identifying tumor beds. We found that tumor tissue had lower transmittance (0.12 ± 0.03) than fibers (0.15 ± 0.04) and fats (0.27 ± 0.07) (P < .01). CONCLUSIONS: HDR-DWI was more sensitive in identifying fiber and tumor tissues than cabinet x-ray and visual observation (P < .01). In addition, HDR-DWI could identify more fibrosis areas than the currently used whole slide imaging did in 12 samples (12/60). We have determined that HDR-DWI can provide more in-depth tumor bed information than x-ray and visual examination do, which will help prevent diagnostic errors in tumor bed sampling.

Radiologic Technologists' Perceived Reasons for Inappropriate Imaging.

PURPOSE: To examine whether radiologic technologists' perceptions of imaging appropriateness differed based on their primary imaging modality, work shift, shift length, and primary practice type. METHODS: A national, cross-sectional study was conducted in the fourth quarter of 2019 using a simple, randomized sample of American Society of Radiologic Technologists (ASRT) members. Study participants were employed in health care settings in radiography, computed tomography (CT), mammography, or radiology leadership. Seven potential reasons for inappropriate imaging procedures (ie, patient expectations, provide patient with a feeling of being taken seriously, lack of time, expectations from relatives, compensation for insufficient clinical examination, normal findings would reassure the patient, and fear of lawsuits) were evaluated for relationships with their primary imaging modality, work shift, shift length, and primary practice type. RESULTS: Disparities in perceived reasons affecting imaging appropriateness were found. Providing the patient with a feeling of being taken seriously was related to primary practice type (P = .022). Lack of time was related to primary imaging modality (P = .005) and primary practice type (P = .006). Expectations from relatives was related to primary imaging modality (P = .016) and primary practice type (P = .027). Compensation for insufficient clinical examination was related to primary imaging modality (P < .001), shift length (P = .011), work shift (P = .002), and primary practice type (P < .001). Fear of lawsuits was related to primary imaging modality (P = .001)) and work shift (P = .002). DISCUSSION: The study reveals that radiologic technologists' perceptions of patient-centered factors and defensive medicine-related factors differ among imaging modalities, shift types, and practice settings. However, more research is required to determine why radiologic technologists perceive these reasons to be present, investigate whether providers feel similarly, and determine perceptual alignment with evidence-based guidelines. CONCLUSION: The findings suggest that attention should focus on the appropriateness of CT imaging procedures performed in hospitals during night shifts.

An estimate of rate of deviation from NCCN guideline recommendations for central nervous system imaging in trials forming basis for drug approval in first line advanced non-small cell lung cancer (NSCLC).

IMPORTANCE: It is unknown whether and to what degree trials submitted to the US FDA to support drug approval adhere to NCCN guideline-recommended care in their baseline and surveillance CNS imaging protocols. OBJECTIVE: We sought to characterize the frequency with which the trials cited in US FDA drug approvals for first line advanced NSCLC between 2015 and 2020 deviated from NCCN guideline-recommended care for baseline and surveillance CNS imaging. DESIGN, SETTING, AND PARTICIPANTS: Retrospective observational analysis using publicly available data of (1) list of trials cited by the FDA in drug approvals for first line advanced NSCLC from 2015 to 2020 (2) individual trial protocols (3) published trial data and supplementary appendices (4) archived versions of the NCCN guidelines for NSCLC from 2009 to 2018 (the years during which the trials were enrolling). MAIN OUTCOMES AND MEASURES: Estimated percentage of trials for first line advanced NSCLC leading to FDA approval which deviated from NCCN guideline-recommended care with regards to CNS baseline and surveillance imaging. RESULTS: A total of 14 studies that had been cited in FDA drug approvals for first line advanced NSCLC met our inclusion criteria between January 1, 2015 and September 30, 2020. Of these trials, 8 (57.1%) deviated from NCCN guidelines in their baseline CNS imaging requirement. The frequency of re-assessment of CNS disease was variable amongst trials as well, with 9 (64.3%) deviating from NCCN recommendations. CONCLUSIONS AND RELEVANCE: The trials supporting US FDA drug approvals in first line advanced NSCLC often have CNS imaging requirements that do not adhere to NCCN guidelines. Many trials permit alternative, substandard methods and the proportion of patients undergoing each modality is uniformly not reported. Nonstandard CNS surveillance protocols are common. To best serve patients with advanced NSCLC in the US, drug approvals by the FDA must be based on trials that mirror clinical practice and have imaging requirements consistent with current US standard of care.

Biomedical Applications of Translational Optical Imaging: From Molecules to Humans.

Light is a powerful investigational tool in biomedicine, at all levels of structural organization. Its multitude of features (intensity, wavelength, polarization, interference, coherence, timing, non-linear absorption, and even interactions with itself) able to create contrast, and thus images that detail the makeup and functioning of the living state can and should be combined for maximum effect, especially if one seeks simultaneously high spatiotemporal resolution and discrimination ability within a living organism. The resulting high relevance should be directed towards a better understanding, detection of abnormalities, and ultimately cogent, precise, and effective intervention. The new optical methods and their combinations needed to address modern surgery in the operating room of the future, and major diseases such as cancer and neurodegeneration are reviewed here, with emphasis on our own work and highlighting selected applications focusing on quantitation, early detection, treatment assessment, and clinical relevance, and more generally matching the quality of the optical detection approach to the complexity of the disease. This should provide guidance for future advanced theranostics, emphasizing a tighter coupling-spatially and temporally-between detection, diagnosis, and treatment, in the hope that technologic sophistication such as that of a Mars rover can be translationally deployed in the clinic, for saving and improving lives.

Separating Hope from Hype: Artificial Intelligence Pitfalls and Challenges in Radiology.

Although recent scientific studies suggest that artificial intelligence (AI) could provide value in many radiology applications, much of the hard engineering work required to consistently realize this value in practice remains to be done. In this article, we summarize the various ways in which AI can benefit radiology practice, identify key challenges that must be overcome for those benefits to be delivered, and discuss promising avenues by which these challenges can be addressed.

Regulatory Issues and Challenges to Artificial Intelligence Adoption.

Artificial intelligence technology promises to redefine the practice of radiology. However, it exists in a nascent phase and remains largely untested in the clinical space. This nature is both a cause and consequence of the uncertain legal-regulatory environment it enters. This discussion aims to shed light on these challenges, tracing the various pathways toward approval by the US Food and Drug Administration, the future of government oversight, privacy issues, ethical dilemmas, and practical considerations related to implementation in radiologist practice.

AGA Clinical Practice Update on Surveillance After Pathologically Curative Endoscopic Submucosal Dissection of Early Gastrointestinal Neoplasia in the United States: Commentary.

The purpose of this American Gastroenterological Association (AGA) Institute Clinical Practice Update was to review the available evidence and provide expert advice regarding surveillance using endoscopy and other relevant modalities after removal of dysplastic lesions and early gastrointestinal cancers with endoscopic submucosal dissection deemed to be pathologically curative. This Clinical Practice Update was commissioned and approved by the AGA Institute Clinical Practice Updates Committee and the AGA Governing Board to provide timely guidance on a topic of high clinical importance to the AGA membership, and underwent internal peer review by the Clinical Practice Updates Committee and external peer review through standard procedures of Gastroenterology. This expert commentary incorporates important as well as recently published studies in this field, and it reflects the experiences of the authors, who are advanced endoscopists with high-level expertise in performing endoscopic submucosal dissection to treat dysplasia and early cancers in the luminal gastrointestinal tract.

Acoustic emissions and kinematic instability of the osteoarthritic knee joint: comparison with radiographic findings.

To evaluate the acoustic emissions (AE) and kinematic instability (KI) of the osteoarthritic (OA) knee joints, and to compare these signals to radiographic findings. Sixty-six female and 43 male participants aged 44-67 were recruited. On radiography, joint-space narrowing, osteophytes and Kellgren-Lawrence (KL) grade were evaluated. Based on radiography, 54 subjects (the study group) were diagnosed with radiographic OA (KL-grade ≥ 2) while the remaining 55 subjects (KL-grade < 2) formed the control group. AE and KI were recorded with a custom-made prototype and compared with radiographic findings using area-under-curve (AUC) and independent T-test. Predictive logistic regression models were constructed using leave-one-out cross validation. In females, the parameters reflecting consistency of the AE patterns during specific tasks, KI, BMI and age had a significant statistical difference between the OA and control groups (p = 0.001-0.036). The selected AE signals, KI, age and BMI were used to construct a predictive model for radiographic OA with AUC of 90.3% (95% CI 83.5-97.2%) which showed a statistical improvement of the reference model based on age and BMI, with AUC of 84.2% (95% CI 74.8-93.6%). In males, the predictive model failed to improve the reference model. AE and KI provide complementary information to detect radiographic knee OA in females.

Contribution of imaging to the diagnosis and follow up of X-linked hypophosphatemia.

X-linked hypophosphatemia (XLH) is the most common form of inheritable rickets. The disease is caused principally by PHEX mutations leading to increased concentrations of circulating intact FGF23, hence renal phosphate wasting, hypophosphatemia, and decreased circulating levels of 1,25(OH)2 vitamin D. The chronic hypophosphatemia leads to rickets and osteomalacia through a combination of mechanisms, including a lack of endochondral ossification and impaired mineralization. Imaging has a major role in determining the diagnosis of rickets and its cause, detecting complications as early as possible, and helping in treatment monitoring.

Multi-scale Xception based depthwise separable convolution for single image super-resolution.

The main target of Single image super-resolution is to recover high-quality or high-resolution image from degraded version of low-quality or low-resolution image. Recently, deep learning-based approaches have achieved significant performance in image super-resolution tasks. However, existing approaches related with image super-resolution fail to use the features information of low-resolution images as well as do not recover the hierarchical features for the final reconstruction purpose. In this research work, we have proposed a new architecture inspired by ResNet and Xception networks, which enable a significant drop in the number of network parameters and improve the processing speed to obtain the SR results. We are compared our proposed algorithm with existing state-of-the-art algorithms and confirmed the great ability to construct HR images with fine, rich, and sharp texture details as well as edges. The experimental results validate that our proposed approach has robust performance compared to other popular techniques related to accuracy, speed, and visual quality.

Oropharyngeal cancer patient stratification using random forest based-learning over high-dimensional radiomic features.

To improve risk prediction for oropharyngeal cancer (OPC) patients using cluster analysis on the radiomic features extracted from pre-treatment Computed Tomography (CT) scans. 553 OPC Patients randomly split into training (80%) and validation (20%), were classified into 2 or 3 risk groups by applying hierarchical clustering over the co-occurrence matrix obtained from a random survival forest (RSF) trained over 301 radiomic features. The cluster label was included together with other clinical data to train an ensemble model using five predictive models (Cox, random forest, RSF, logistic regression, and logistic-elastic net). Ensemble performance was evaluated over the independent test set for both recurrence free survival (RFS) and overall survival (OS). The Kaplan-Meier curves for OS stratified by cluster label show significant differences for both training and testing (p val < 0.0001). When compared to the models trained using clinical data only, the inclusion of the cluster label improves AUC test performance from .62 to .79 and from .66 to .80 for OS and RFS, respectively. The extraction of a single feature, namely a cluster label, to represent the high-dimensional radiomic feature space reduces the dimensionality and sparsity of the data. Moreover, inclusion of the cluster label improves model performance compared to clinical data only and offers comparable performance to the models including raw radiomic features.

NCI Imaging Data Commons.

The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with nonimaging data. We achieve this goal by colocating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research. SIGNIFICANCE: This study introduces NCI Imaging Data Commons, a new repository of the NCI Cancer Research Data Commons, which will support cancer imaging research on the cloud.

Expert opinion paper on cardiac imaging after ischemic stroke.

This expert opinion paper on cardiac imaging after acute ischemic stroke or transient ischemic attack (TIA) includes a statement of the "Heart and Brain" consortium of the German Cardiac Society and the German Stroke Society. The Stroke Unit-Commission of the German Stroke Society and the German Atrial Fibrillation NETwork (AFNET) endorsed this paper. Cardiac imaging is a key component of etiological work-up after stroke. Enhanced echocardiographic tools, constantly improving cardiac computer tomography (CT) as well as cardiac magnetic resonance imaging (MRI) offer comprehensive non- or less-invasive cardiac evaluation at the expense of increased costs and/or radiation exposure. Certain imaging findings usually lead to a change in medical secondary stroke prevention or may influence medical treatment. However, there is no proof from a randomized controlled trial (RCT) that the choice of the imaging method influences the prognosis of stroke patients. Summarizing present knowledge, the German Heart and Brain consortium proposes an interdisciplinary, staged standard diagnostic scheme for the detection of risk factors of cardio-embolic stroke. This expert opinion paper aims to give practical advice to physicians who are involved in stroke care. In line with the nature of an expert opinion paper, labeling of classes of recommendations is not provided, since many statements are based on expert opinion, reported case series, and clinical experience.

Quality management in imaging diagnosis and the nursing team: a case study.

OBJECTIVE: to know the contributions of nursing in the implementation of the quality management principle of the accreditation program in imaging diagnosis. METHODS: a single, qualitative case study carried out in an accredited radiology and imaging diagnosis service. The data collection took place through semi-structured interviews, direct observation, and documentary analysis with the support of software in organizing the data for analysis. RESULTS: a total of four thematic units emerged: the accreditation process in imaging services, the implementation of the program, the role of nursing in imaging services and patient safety and the management of non-conformities in imaging services. Of the other data sources, the word risk was highlighted and a non-conformity was evidenced in the external audit. FINAL CONSIDERATIONS: nursing contributed mainly to the management of the risks involved in the performance of imaging and patient safety tests, requirements of the quality management principle of the accreditation program.

Should We Scan Hidradenitis Suppurativa Patients? A Systematic Review of Radiologic Findings.

OBJECTIVE: To bring awareness and close gaps between dermatologists and radiologists about the contribution of imaging techniques for diagnosis, treatment, and follow-up of hidradenitis suppurativa (HS). DATA SOURCES: Investigators searched the PubMed database for articles on HS and radiology techniques. STUDY SELECTION: Databases were searched up to December 2018. The query retrieved 257 publications, of which 103 were unique; of these, 7 were inaccessible. From the remaining 96, 33 were irrelevant (did not discuss HS lesion features). After applying the inclusion criteria, 63 studies were relevant to this study. DATA EXTRACTION: A standardized form was constructed to extract data from eligible studies by two independent authors. DATA SYNTHESIS: Imaging techniques are significant and useful tools in HS management. Imaging should be carried out to evaluate disease severity, subclinical features, treatment success, and intraoperative patient assessment. Providers should consider nonconventional radiology techniques, which are underused in clinical management of HS. Further, dermatology and radiology require a shared terminology of disease features to better understand patient status. CONCLUSIONS: Publications on HS lesion imaging have increased over the years. Imaging techniques have proven useful for determining HS severity and treatment effectiveness, as well as intraoperative patient assessment. These authors strongly recommend the use of these techniques in routine clinical practice for patients with HS.

Radiation Reduction Strategies in Pediatric Orthopaedics.

INTRODUCTION: Pediatric orthopaedic patients have the potential for significant radiation exposure from the use of imaging studies, such as computed tomography and bone scintigraphy. With the potential for long-term treatment, such as is required for scoliosis or osteogenesis imperfecta, patients are at even greater risk of radiation-induced carcinogenesis. DISCUSSION: Although an association between radiation and cancer risk is evident, causation is difficult to prove because comorbidities or genetic predispositions may play a role in the higher baseline rates of malignancy later in life. Efforts have been made over the years to reduce exposure using more modern imaging techniques and simple radiation reduction strategies. Educational efforts and clinical practice guidelines are decreasing the rate of computed tomography scan use in pediatrics. Although considerable work is being done on the development of radiation-free imaging modalities, imaging that uses ionizing radiation will, in the near term, be necessary in specific circumstances to provide optimal care to pediatric orthopaedic patients. CONCLUSION: Knowledge of the ionizing radiation exposure associated with commonly used tests as well as radiation-reduction strategies is essential for the optimal and safe care of pediatric orthopaedic patients.