This study explores the impact of physician experience, specialty, and institutional background on the performance of AI-assisted assessment of treatment responses in bladder cancer patients. Utilizing pre- and post-chemotherapy CTU scans from 123 patients, 17 physicians with varying levels of experience and from different specialties and institutions assessed 157 lesion pairs. The lesion pairs were divided into easy and difficult cases to evaluate the AI system's effectiveness in different scenarios. The study revealed that AI assistance significantly improved diagnostic accuracy in easy cases for both experienced and inexperienced physicians, with a great benefit observed in radiologists and oncologists. In difficult cases, the AI's impact was present but less pronounced, indicating that while AI can enhance performance in challenging situations, its effectiveness is more limited in complex cases. Additionally, the study found that institutional background influenced the effectiveness of AI assistance, suggesting that certain training or cultural factors may affect physicians' trust in AI recommendations. The findings underscore the potential of AI to support clinical decision-making in bladder cancer treatment response assessment, particularly in less complex cases. However, they also highlight the need for tailored implementation and user training of AI systems to maximize their effectiveness across different medical specialties and institutions. By aligning AI tools with the specific needs and expertise of physicians, their confidence and efficacy in using AI in complex medical scenarios can be enhanced.

In this chapter, we will describe practical approaches to the evaluation of adrenal masses detected as incidental masses, as well as in other clinical scenarios in the oncological and non-oncological patient. The most commonly used clinical and imaging techniques and procedures used in the evaluation of these masses and their common appearances will also be described. Current European and US guidelines and their limitations will be briefly addressed.

The clinical and imaging presentation of pancreatic neuroendocrine tumors (PanNETs) is variable and depends on tumor grade, stage, and functional status. This degree of variability combined with a multitude of treatment options and imaging modalities results in complexity when choosing the most appropriate imaging studies across various clinical scenarios. While various guidelines exist in the management and evaluation of PanNETs, there is an overall lack of consensus and detail regarding optimal imaging guidelines and protocols. This manuscript aims to fill gaps where current guidelines may lack specificity regarding the choice of the most appropriate imaging study in the diagnosis, treatment planning, monitoring, and surveillance of PanNETs under various clinical scenarios.

PURPOSE:Standardized reporting in radiology has an established role in numerous disease processes, with added benefits in oncology of reduced variability, and generation of a thorough and pertinent report with a focused and relevant conclusion. Many radiologists are not familiar with the imaging patterns of neuroendocrine neoplasm (NEN) spread and recurrence. This paper will present standardized CT, MRI, and PET templates for reporting gastroenteropancreatic (GEP) NENs and explain the rationale for including specific pertinent positive and negative findings, at various stages of disease management, based on site of origin.METHODS:Basic templates for initial and follow-up anatomic and molecular GEP NEN imaging were created with input from the multidisciplinary Society of Abdominal Radiology (SAR) Neuroendocrine Tumor Disease Focused Panel (NET-DFP). The templates were further modified and finalized after several iterations.RESULTS:Four main report templates were generated for (i) initial anatomic CT or MR imaging studies, (ii) follow-up anatomic CT or MR imaging studies, (iii) initial Somatostatin Receptor (SSTR) or FDG PET imaging studies, and (iv) follow-up SSTR or FDG PET imaging studies. Each study template was formatted to allow its integration into a dictation software directly and be modified as needed, with internalized instructions indicating where a drop-down menu or macro may be used to personalize the template as necessary.CONCLUSION:These templates were created through a combination of multidisciplinary expert opinion discussion supported by literature review and provide basic structured reporting standards for GEP NEN anatomic and molecular imaging studies.

We have previously developed a computerized decision support system for bladder cancer treatment response assessment (CDSS-T) in CT urography (CTU). In this work, we conducted an observer study to evaluate the diagnostic accuracy and intra-observer variability with and without the CDSS-T system. One hundred fifty-seven pre- and posttreatment lesion pairs were identified in pre- and post- chemotherapy CTU scans of 123 patients. Forty lesion pairs had T0 stage (complete response) after chemotherapy. Multi-disciplinary observers from 4 different institutions participated in reading the lesion pairs, including 5 abdominal radiologists, 4 radiology residents, 5 oncologists, 1 urologist, and 1 medical student. Each observer provided estimates of the T0 likelihood after treatment without and then with the CDSST aid for each lesion. To assess the intra-observer variability, 51 cases were evaluated two times – the original and the repeated evaluation. The average area under the curve (AUC) of 16 observers for estimation of T0 disease after treatment increased from 0.73 without CDSS-T to 0.77 with CDSS-T (p = 0.003). For the evaluation with CDSS-T, the average AUC performance for different institutions was similar. The performance with CDSS-T was improved significantly and the AUC standard deviations were slightly smaller showing potential trend of more accurate and uniform performance with CDSS-T. There was no significant difference between the original and repeated evaluation. This study demonstrated that our CDSS-T system has the potential to improve treatment response assessment of physicians from different specialties and institutions, and reduce the inter- and intra-observer variabilities of the assessments.

Male genitourinary neuroendocrine neoplasms (GU-NENs) are rare, without any definite imaging characteristics. The WHO classified neuroendocrine neoplasms in the 2016 classification of the tumors of the urinary tract and genital organs along with other GU tumors; however, no pathologic grading system is available as published for gastroenteropancreatic neuroendocrine neoplasms. Often a multimodality approach using cross-sectional imaging techniques, such as molecular imaging and histopathology are implemented to arrive at the diagnosis. This article provides a review of the pathology and imaging features of the male GU-NENs.

Gastric neuroendocrine neoplasms are uncommon tumors with variable differentiation and malignant potential. Three main subtypes are recognized: type 1, related to autoimmune atrophic gastritis; type 2, associated with Zollinger–Ellison and MEN1 syndrome; and type 3, sporadic. Although endoscopy alone is often sufficient for diagnosis and management of small, indolent, multifocal type 1 tumors, imaging is essential for evaluation of larger, high-grade, and type 2 and 3 neoplasms. Hypervascular intraluminal gastric masses are typically seen on CT/MRI, with associated perigastric lymphadenopathy and liver metastases in advanced cases. Somatostatin receptor nuclear imaging (such as Ga-68-DOTATATE PET/CT) may also be used for staging and assessing candidacy for peptide receptor radionuclide therapy. Radiotracer uptake is more likely in well-differentiated, lower-grade tumors, and less likely in poorly differentiated tumors, for which F-18-FDG-PET/CT may have additional value. Understanding disease pathophysiology and evolving histologic classifications is particularly useful for radiologists, as these influence tumor behavior, preferred imaging, therapy options, and patient prognosis.

Pancreatic neuroendocrine neoplasms (PaNENs) are a unique group of pancreatic neoplasms with a wide range of clinical presentations and behaviors. Given their heterogeneous appearance and increasing detection on cross-sectional imaging, it is essential that radiologists understand the variable presentation and distinctions PaNENs display compared to other pancreatic neoplasms. Additionally, some of these neoplasms may be hormonally functional, and it is imperative that radiologists be aware of the common clinical presentations of hormonally active PaNENs. Knowledge of PaNEN pathology and treatments may influence which imaging modality is optimal for each patient. Each imaging modality used for PaNENs has distinct advantages and disadvantages, particularly in different treatment settings. Thus, the focus of this manuscript is to provide an update for the radiologist on PaNEN pathology, imaging, and treatments.

This observer study investigates the effect of computerized artificial intelligence (AI)-based decision support system (CDSS-T) on physicians' diagnostic accuracy in assessing bladder cancer treatment response. The performance of 17 observers was evaluated when assessing bladder cancer treatment response without and with CDSS-T using pre- and post-chemotherapy CTU scans in 123 patients having 157 pre- and post-treatment cancer pairs. The impact of cancer case difficulty, observers' clinical experience, institution affiliation, specialty, and the assessment times on the observers' diagnostic performance with and without using CDSS-T were analyzed. It was found that the average performance of the 17 observers was significantly improved (p = 0.002) when aided by the CDSS-T. The cancer case difficulty, institution affiliation, specialty, and the assessment times influenced the observers' performance without CDSS-T. The AI-based decision support system has the potential to improve the diagnostic accuracy in assessing bladder cancer treatment response and result in more consistent performance among all physicians.

We evaluated whether a computerized decision support system for bladder cancer treatment response assessment (CDSS-T) can assist physicians from different institutions in identifying patients who have complete response after neoadjuvant chemotherapy. Pre- and post-chemotherapy CTU scans of 96 patients (114 pre- and post-treatment lesion pairs) were collected retrospectively. The pathological cancer stage after treatment was collected as the reference standard of response to treatment. 24% of the lesion pairs had T0 cancer stage (complete response) after chemotherapy. Our CDSST that combined DL-CNN and radiomics features was trained to distinguish between T0 and <T0 cases. Five abdominal radiologists and 3 oncologists participated in the observer study. One radiologist and one oncologist were from external institutions. All physicians estimated the likelihood of stage T0 disease after treatment by viewing each pre-post-treatment CTU pair displayed side by side on a specialized graphical user interface. The observer provided an estimate without CDSS-T first and then might revise the estimate, if preferred, after the CDSS-T score was displayed. The observers’ estimates with and without CDSS-T were analyzed with multi-reader, multi-case (MRMC) methodology. The AUC for prediction of T0 disease after treatment was 0.85±0.04 for the CDSS-T alone. The performance of all but one observers increased with the aid of CDSS-T. The average AUC for the observers was 0.77 (range: 0.69-0.83) without CDSS-T, and increased to 0.80 (range: 0.72-0.86), (p = 0.006) with CDSS-T. The CDSS-T could improve the performance of radiologists and oncologists from different institutions in identifying patients who fully responded to treatment. There was no apparent difference in the performance of the physicians from different institutions.

We evaluated the intraobserver variability of physicians aided by a computerized decision-support system for treatment response assessment (CDSS-T) to identify patients who show complete response to neoadjuvant chemotherapy for bladder cancer, and the effects of the intraobserver variability on physicians' assessment accuracy. A CDSS-T tool was developed that uses a combination of deep learning neural network and radiomic features from computed tomography (CT) scans to detect bladder cancers that have fully responded to neoadjuvant treatment. Pre- and postchemotherapy CT scans of 157 bladder cancers from 123 patients were collected. In a multireader, multicase observer study, physician-observers estimated the likelihood of pathologic T0 disease by viewing paired pre/posttreatment CT scans placed side by side on an in-house-developed graphical user interface. Five abdominal radiologists, 4 diagnostic radiology residents, 2 oncologists, and 1 urologist participated as observers. They first provided an estimate without CDSS-T and then with CDSS-T. A subset of cases was evaluated twice to study the intraobserver variability and its effects on observer consistency. The mean areas under the curves for assessment of pathologic T0 disease were 0.85 for CDSS-T alone, 0.76 for physicians without CDSS-T and improved to 0.80 for physicians with CDSS-T (P = .001) in the original evaluation, and 0.78 for physicians without CDSS-T and improved to 0.81 for physicians with CDSS-T (P = .010) in the repeated evaluation. The intraobserver variability was significantly reduced with CDSS-T (P < .0001). The CDSS-T can significantly reduce physicians' variability and improve their accuracy for identifying complete response of muscle-invasive bladder cancer to neoadjuvant chemotherapy.

There have been many publications detailing imaging features of malignant transformation of intraductal papillary mucinous neoplasms (IPMN), management and recommendations for imaging follow-up of diagnosed or presumed IPMN. However, there is no consensus on several practical aspects of imaging IPMN that could serve as a clinical guide for radiologists and enable future data mining for research. These aspects include how to measure IPMN, define reporting terminology, standardize reporting and unify guidelines for surveillance. The Society of Abdominal Radiology (SAR) created multiple Disease-Focused Panels (DFP) comprised multidisciplinary panel members who focus on a particular disease, with the goal to develop ways for radiologists to improve patient care, education, and research. DFP members met to identify the current controversies and limitations of imaging pancreatic IPMN. This paper aims to provide a practical review of the key imaging characteristics of IPMN for trainees and practicing radiologists, to guide uniformity of performance and interpretation of surveillance imaging studies, and to improve communication with clinicians by providing a lexicon and reporting template based on the experience of the SAR-DFP panel members.

Primary tumours originating in the retroperitoneum are extremely uncommon, with the vast majority of these tumours being malignant. They can be divided into three major groups: Tumours arising from mesenchymal cells (soft tissue sarcomas) Tumours arising from germ cells (extragonadal germ cell tumours) Tumours arising from haematopoietic cells (lymphoma)

OBJECTIVE. The purposes of this study were to investigate factors driving callback MRI and CT examinations and to discern opportunities for optimizing the patient experience by reducing future callbacks. MATERIALS AND METHODS. All consecutive outpatient CT and MRI callback examinations from October 2015 to October 2017 in four radiology subspecialties (cardiothoracic imaging, abdominal imaging, neuroradiology, musculoskeletal imaging) were reviewed at an academic quaternary care center. Callback details (modality, subspecialty, protocoling radiologist, protocol assigned, protocol performed, interpreting radiologist, and reason for callback) were recorded, and reason for callback was categorized. Callback rates were calculated and compared across subspecialties and modalities. RESULTS. There were 194 callbacks among 147,068 MRI and 195,578 CT examinations. The callback rate for MRI was approximately nine times that of CT (MRI, 0.114% [n = 168]; CT, 0.013% [n = 26]). The callback rate was highest for musculoskeletal radiology (CT, 0.090% [7/7802]; MRI, 0.265% [73/27501]; p < 0.0001). Of 65 subspecialty radiologists, nine initiated 52% (101/194) of all callback examinations, and 20 initiated 80% (155/194). One musculoskeletal radiologist was responsible for 11.8% (23/194) of all callbacks. The most common reasons for callbacks were protocol error (28% [55/194]), inadequate anatomic coverage (21% [40/194]), incomplete examination (13% [25/194]), and perceived suboptimal image quality (11% [22/194]). The three most common causes of callbacks (62% [120/194] of all callbacks) were largely preventable. CONCLUSION. Outpatient callback examinations are uncommon, occur more often for MRI than CT, and are often preventable. Callback proclivities likely vary between attending radiologists. Targeted improvement efforts may mitigate callbacks.

BACKGROUND:Objective radiographic assessment is crucial for accurately evaluating therapeutic efficacy and patient outcomes in oncology clinical trials. Imaging assessment workflow can be complex; can vary with institution; may burden medical oncologists, who are often inadequately trained in radiology and response criteria; and can lead to high interobserver variability and investigator bias. This article reviews the development of a tumor response assessment core (TRAC) at a comprehensive cancer center with the goal of providing standardized, objective, unbiased tumor imaging assessments, and highlights the web-based platform and overall workflow. In addition, quantitative response assessments by the medical oncologists, radiologist, and TRAC are compared in a retrospective cohort of patients to determine concordance. PATIENTS AND METHODS:The TRAC workflow includes an image analyst who pre-reviews scans before review with a board-certified radiologist and then manually uploads annotated data on the proprietary TRAC web portal. Patients previously enrolled in 10 lung cancer clinical trials between January 2005 and December 2015 were identified, and the prospectively collected quantitative response assessments by the medical oncologists were compared with retrospective analysis of the same dataset by a radiologist and TRAC. RESULTS:This study enlisted 49 consecutive patients (53% female) with a median age of 60 years (range, 29-78 years); 2 patients did not meet study criteria and were excluded. A linearly weighted kappa test for concordance for TRAC versus radiologist was substantial at 0.65 (95% CI, 0.46-0.85; standard error [SE], 0.10). The kappa value was moderate at 0.42 (95% CI, 0.20-0.64; SE, 0.11) for TRAC versus oncologists and only fair at 0.34 (95% CI, 0.12-0.55; SE, 0.11) for oncologists versus radiologist. CONCLUSIONS:Medical oncologists burdened with the task of tumor measurements in patients on clinical trials may introduce significant variability and investigator bias, with the potential to affect therapeutic response and clinical trial outcomes. Institutional imaging cores may help bridge the gap by providing unbiased and reproducible measurements and enable a leaner workflow.

Response Evaluation Criteria in Solid Tumors (RECIST), including version 1.0 and 1.1, has been universally accepted as the standard response assessment criteria for conventional chemotherapies. Increasing use of immunotherapy led to the need and development of immune-related RECIST. Imaging plays a crucial role in response assessment for solid tumors in guiding patient management as well as in clinical trials. Familiarity to different response criteria will help radiologists to optimally identify, select, and measure tumor lesions per the criteria and assess response to therapy. This article provides a comprehensive review of published RECIST criteria.

The aim of the study was to identify the frequency of isolated pelvic metastasis with the goal of determining the utility of pelvic CT as a surveillance strategy in patients with resected biliary tract cancer (BTC). Study eligibility criteria included patients 18 years or older with BTC who underwent R0 or R1 surgical resection at University of Michigan between 2004 and 2018, with a minimum 6-month disease-free surveillance period. CT and MRI reports were independently graded by two radiologists as positive (organ metastasis, peritoneal carcinomatosis, or enlarged lymph nodes), equivocal (borderline lymph nodes or non-nodular ascites), or negative (absence of or benign findings) in the abdomen and pelvis separately. A 3rd blinded radiologist reviewed all positive and equivocal scans. Clinic notes were reviewed to identify new or worsening signs and symptoms that would warrant an earlier pelvic surveillance scan. A 95% binomial proportion confidence interval was used to find the probability of isolated pelvic metastasis. BTC were anatomically classified as extra-hepatic (distal and hilar) cholangiocarcinoma (38; 25%), intra-hepatic cholangiocarcinoma (57; 38%), and gallbladder cancer (56; 37%). 151 patients met eligibility criteria, of which 123 (81%) had no pelvic metastasis, 51 (34%) had localized upper abdominal metastasis, and 23 (15%) had concomitant abdominal and pelvic metastasis. Median follow-up time was 19.2 months. One (0%) subject with resected BTC (intra-hepatic) developed isolated osseous pelvic metastasis during surveillance (95% CI 0.004–0.1; p = 0.0003). 3 (2%) subjects developed isolated simple ascites (equivocal grade) without concurrent upper abdominal metastasis. Isolated pelvic metastasis is a rare occurrence during surveillance in patients with resected BTCs, and therefore, follow-up pelvic CT in absence of specific symptoms may be unnecessary.

Hypervascular pancreatic lesions/masses can arise due to a variety of causes, both benign and malignant, leading to a wide differential diagnosis. Accurate differentiation of these lesions into appropriate diagnoses can be challenging; however, this is important for directing clinical management. This manuscript provides a multimodality imaging review of hypervascular pancreatic lesion, with emphasis on an imaging-based algorithmic approach for differentiation of these lesions, which may serve as a decision support tool when encountering these uncommon lesions. Additionally, we stratify these lesions into three categories based on malignant potential, to help guide clinical management.

美国放射学院(American College of Radiology,ACR)偶发病变委员会(Incidental Findings Committee,IFC)针对CT和MRI上偶然发现的肾上腺肿块,提出了新的管理建议.该建议是对美国放射学院杂志(JACR) 2010版肾上腺、肾脏、肝脏及胰腺偶发病变管理白皮书中肾上腺部分的更新.由腹部放射科医师和1位内分泌外科医生组成的肾上腺亚组委员会制定了该方案.该方案结合已发表的文献和专家意见,经过反复推敲最终达成共识.该方案依据患者的特征和图像的特点对偶然发现的肾上腺肿块进行分类.对于每种特定的组合,总结良性或惰性的特征(足以终止随访),并提出后续管理建议.该方案涵盖了很多、但并非适用于所有的病理及临床情况,旨在通过提供肾上腺偶发肿块的管理意见以提升医疗质量.