Integrated Automatic Examination Assignment Reduces Radiologist Interruptions: A 2-Year Cohort Study of 232,022 Examinations.

Radiology departments face challenges in delivering timely and accurate imaging reports, especially in high-volume, subspecialized settings. In this retrospective cohort study at a tertiary cancer center, we assessed the efficacy of an Automatic Assignment System (AAS) in improving radiology workflow efficiency by analyzing 232,022 CT examinations over a 12-month period post-implementation and compared it to a historical control period. The AAS was integrated with the hospital-wide scheduling system and set up to automatically prioritize and distribute unreported CT examinations to available radiologists based on upcoming patient appointments, coupled with an email notification system. Following this AAS implementation, despite a 9% rise in CT volume, coupled with a concurrent 8% increase in the number of available radiologists, the mean daily urgent radiology report requests (URR) significantly decreased by 60% (25 ± 12 to 10 ± 5, t = -17.6, p < 0.001), and URR during peak days (95th quantile) was reduced by 52.2% from 46 to 22 requests. Additionally, the mean turnaround time (TAT) for reporting was significantly reduced by 440 min for patients without immediate appointments and by 86 min for those with same-day appointments. Lastly, patient waiting time sampled in one of the outpatient clinics was not negatively affected. These results demonstrate that AAS can substantially decrease workflow interruptions and improve reporting efficiency.

Bridging Communication Gaps Between Radiologists, Referring Physicians, and Patients Through Standardized Structured Cancer Imaging Reporting: The Experience with Female Pelvic MRI Assessment Using O-RADS and a Simulated Cohort Patient Group.

RATIONALE AND OBJECTIVES: This study aimed to evaluate whether implementing structured reporting based on Ovarian-Adnexal Reporting and Data System (O-RADS) magnetic resonance imaging (MRI) in women with sonographically indeterminate adnexal masses improves communication between radiologists, referrers, and patients/caregivers and enhances diagnostic performance for determining adnexal malignancy. MATERIALS AND METHODS: We retrospectively analyzed prospectively issued MRI reports in 2019-2022 performed for characterizing adnexal masses before and after implementing O-RADS MRI; 56 patients/caregivers and nine gynecologic oncologists ("referrers") were surveyed about report interpretability/clarity/satisfaction; responses for pre- and post-implementation reports were compared using Fisher's exact and Chi-squared tests. Diagnostic performance was assessed using receiver operating characteristic curves. RESULTS: A total of 123 reports from before and 119 reports from after O-RADS MRI implementation were included. Survey response rates were 35.7% (20/56) for patients/caregivers and 66.7% (6/9) for referrers. For patients/caregivers, O-RADS MRI reports were clearer (p < 0.001) and more satisfactory (p < 0.001) than unstructured reports, but interpretability did not differ significantly (p = 0.14), as 28.0% (28/100) of postimplementation and 38.0% (38/100) of preimplementation reports were considered difficult to interpret. For referrers, O-RADS MRI reports were clearer, more satisfactory, and easier to interpret (p < 0.001); only 1.3% (1/77) were considered difficult to interpret. For differentiating benign from malignant adnexal lesions, O-RADS MRI showed area under the curve of 0.92 (95% confidence interval [CI], 0.85-0.99), sensitivity of 0.81 (95% CI, 0.58-0.95), and specificity of 0.91 (95% CI, 0.83-0.96). Diagnostic performance of reports before implementation could not be calculated due to many different phrases used to describe the likelihood of malignancy. CONCLUSION: Implementing standardized structured reporting using O-RADS MRI for characterizing adnexal masses improved clarity and satisfaction for patients/caregivers and referrers. Interpretability improved for referrers but remained limited for patients/caregivers.

Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis.

BACKGROUND: The rising global cancer burden has led to an increasing demand for imaging tests such as [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [18F]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. METHODS: In this retrospective analysis we collected 16 583 [18F]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [18F]FDG-PET-CT binary classification (Deauville 1-3 vs 4-5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [18F]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. FINDINGS: In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0·949 (95% CI 0·942-0·956), accuracy of 0·890 (0·879-0·901), sensitivity of 0·868 (0·851-0·885), and specificity of 0·913 (0·899-0·925); LARS-max achieved an AUC of 0·949 (0·942-0·956), accuracy of 0·868 (0·858-0·879), sensitivity of 0·909 (0·896-0·924), and specificity of 0·826 (0·808-0·843); and LARS-ptct achieved an AUC of 0·939 (0·930-0·948), accuracy of 0·875 (0·864-0·887), sensitivity of 0·836 (0·817-0·855), and specificity of 0·915 (0·901-0·927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0·953 (0·938-0·966), accuracy of 0·907 (0·888-0·925), sensitivity of 0·874 (0·843-0·904), and specificity of 0·949 (0·921-0·960); LARS-max achieved an AUC of 0·952 (0·937-0·965), accuracy of 0·898 (0·878-0·916), sensitivity of 0·899 (0·871-0·926), and specificity of 0·897 (0·871-0·922); and LARS-ptct achieved an AUC of 0·932 (0·915-0·948), accuracy of 0·870 (0·850-0·891), sensitivity of 0·827 (0·793-0·863), and specificity of 0·913 (0·889-0·937). INTERPRETATION: Deep learning accurately distinguishes between [18F]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. FUNDING: National Institutes of Health-National Cancer Institute Cancer Center Support Grant.

A preoperative nomogram incorporating CT to predict the probability of ovarian clear cell carcinoma.

OBJECTIVES: To evaluate clinical, laboratory, and radiological variables from preoperative contrast-enhanced computed tomography (CECT) for their ability to distinguish ovarian clear cell carcinoma (OCCC) from non-OCCC and to develop a nomogram to preoperatively predict the probability of OCCC. METHODS: This IRB-approved, retrospective study included consecutive patients who underwent surgery for an ovarian tumor from 1/1/2000 to 12/31/2016 and CECT of the abdomen and pelvis ≤90 days before primary debulking surgery. Using a standardized form, two experienced oncologic radiologists independently analyzed imaging features and provided a subjective 5-point impression of the probability of the histological diagnosis. Nomogram models incorporating clinical, laboratory, and radiological features were created to predict histological diagnosis of OCCC over non-OCCC. RESULTS: The final analysis included 533 patients with surgically confirmed OCCC (n = 61) and non-OCCC (n = 472); history of endometriosis was more often found in patients with OCCC (20% versus 3.6%; p < 0.001), while CA-125 was significantly higher in patients with non-OCCC (351 ng/mL versus 70 ng/mL; p < 0.001). A nomogram model incorporating clinical (age, history of endometriosis and adenomyosis), laboratory (CA-125) and imaging findings (peritoneal implant distribution, morphology, laterality, and diameter of ovarian lesion and of the largest solid component) had an AUC of 0.9 (95% CI: 0.847, 0.949), which was comparable to the AUCs of the experienced radiologists' subjective impressions [0.8 (95% CI: 0.822, 0.891) and 0.9 (95% CI: 0.865, 0.936)]. CONCLUSIONS: A presurgical nomogram model incorporating readily accessible clinical, laboratory, and CECT variables was a powerful predictor of OCCC, a subtype often requiring a distinctive treatment approach.

Fat-containing adnexal masses on MRI: solid tissue volume and fat distribution as a guide for O-RADS Score assignment.

PURPOSE: To explore ways to improve O-RADS MRI scoring for fat-containing adnexal masses, by investigating methods for quantifying solid tissue volume and fat distribution and evaluating their associations with malignancy. METHODS: This retrospective, single-center study included patients with fat-containing adnexal masses on MRI during 2008-2021. Two radiologists independently reviewed overall size (Sizeoverall), size of any solid tissue (Sizeanysolid), size of solid tissue that was not Rokitansky nodule (Sizenon-Rokitansky), and fat distribution. Wilcoxon test, Fisher-exact test, and ROC curve analysis were performed. Reference standard was pathology or follow-up > 24 months. RESULTS: 188 women (median age 35 years) with 163 benign and 25 malignant lesions were included. Sizeoverall (R1, 9.9 cm vs 5.9 cm; R2, 12.4 cm vs 6.0 cm), Sizeanysolid (R1, 5.1 cm vs 1.2 cm; R2, 3.2 cm vs 0.0 cm), Sizenon-Rokitansky (R1, 5.1 cm vs 0.0 cm; R2, 3.1 cm vs 0.0 cm), and fat distribution differed significantly between malignant and benign lesions (p < 0.01). Area under ROC curve was greatest using Sizenon-Rokitansky (R1, 0.83; R2, 0.86) vs Sizeoverall (R1, 0.78; R2, 0.81) or Sizeanysolid (R1, 0.79; R2, 0.81), though differences were non-significant (p = 0.48-0.93). Cutoffs for Sizenon-Rokitansky (R1, ≥ 1.2 cm; R2, ≥ 1.0 cm) yielded sensitivity and specificity of 0.72 and 0.93 (R1) and 0.76 and 0.95 (R2). Among immature teratomas, 85.7% displayed scattered fat. CONCLUSION: Overall size, size of (any or non-Rokitansky-nodule) solid tissue, and fat distribution differed between benign and malignant fat-containing adnexal masses. Incorporating these would constitute simple and practical approaches to refining O-RADS MRI scoring.

Programmatic Implementation of a Custom Subspecialized Oncologic Imaging Workflow Manager at a Tertiary Cancer Center.

PURPOSE: To evaluate whether a custom programmatic workflow manager reduces reporting turnaround times (TATs) from a body oncologic imaging workflow at a tertiary cancer center. METHODS: A custom software program was developed and implemented in the programming language R. Other aspects of the workflow were left unchanged. TATs were measured over a 12-month period (June-May). The same prior 12-month period served as a historical control. Median TATs of magnetic resonance imaging (MRI) and computed tomography (CT) examinations were compared with a Wilcoxon test. A chi-square test was used to compare the numbers of examinations reported within 24 hours and after 72 hours as well as the proportions of examinations assigned according to individual radiologist preferences. RESULTS: For all MRI and CT examinations (124,507 in 2019/2020 and 138,601 in 2020/2021), the median TAT decreased from 4 (interquartile range: 1-22 hours) to 3 hours (1-17 hours). Reports completed within 24 hours increased from 78% (124,127) to 89% (138,601). For MRI, TAT decreased from 22 (5-49 hours) to 8 hours (2-21 hours), and reports completed within 24 hours increased from 55% (14,211) to 80% (23,744). For CT, TAT decreased from 3 (1-19 hours) to 2 hours (1-13 hours), and reports completed within 24 hours increased from 84% (82,342) to 92% (99,922). Delayed reports (with a TAT > 72 hours) decreased from 17.0% (4,176) to 2.2% (649) for MRI and from 2.5% (2,500) to 0.7% (745) for CT. All differences were statistically significant (P < .001). CONCLUSION: The custom workflow management software program significantly decreased MRI and CT report TATs.

MRI-Detectability of Clinically Significant Prostate Cancer Relates to Oncologic Outcomes After Prostatectomy.

INTRODUCTION/BACKGROUND: Magnetic resonance imaging (MRI) misses a proportion of "clinically significant" prostate cancers (csPC) as defined by histopathology criteria. The aim of this study was to analyze whether long-term oncologic outcomes differ between MRI-detectable and MRI-occult csPC. PATIENTS AND METHODS: Retrospective analysis of 1449 patients with pre-prostatectomy MRI and csPC on prostatectomy specimens (ie, Grade group ≥2 or extraprostatic spread) between 2001-2006. T2-weighted MRIs were classified according to the Prostate Imaging Reporting and Data System into MRI-occult (categories 1, 2), MRI-equivocal (category 3), and MRI-detectable (categories 4, 5). Cumulative incidence of biochemical recurrence (BCR), metastatic disease, and cancer-specific mortality, estimated with competing risk models. The median follow-up in survivors was 11.0 years (IQR: 8.9-13.1). RESULTS: In 188 (13%) cases, csPC was MRI-occult, 435 (30%) MRIs were equivocal, and 826 (57%) csPC were MRI-detectable. The 15-year cumulative incidence [95% CI] of BCR was 8.3% [2.2, 19.5] for MRI-occult cases, 17.4% [11.1, 24.8] for MRI-equivocal cases, and 43.3% [38.7, 47.8] for MRI-detectable cases (P < .001). The cumulative incidences of metastases were 0.61% [0.06, 3.1], 3.5% [1.5, 6.9], and 19.6% [15.4, 24.2] for MRI-occult, MRI-equivocal, and MRI-detectable cases, respectively (P < .001). There were no deaths from prostate cancer observed in patients with MRI-occult csPC, compared to an estimated 1.9% [0.54, 4.9], and 7.1 % [4.5, 10.6] for patients with MRI-equivocal and MRI-detectable cancer, respectively (P < .001). CONCLUSION: Oncologic outcomes after prostatectomy for csPC differ between MRI-occult and MRI-detectable lesions. Judging the clinical significance of a negative prostate MRI based on histopathologic surrogates alone might be misleading. MICROABSTRACT: Among 1449 patients with pre-prostatectomy MRI and clinically significant prostate cancer on prostatectomy histopathology, MRI-occult cancers (n = 188, 13%) were less likely to recur biochemically (8% vs. 43%, P < .001), metastasize (0.6% vs. 20%, P < .001), or lead to prostate cancer mortality (0% vs. 7%, P < .001) than MRI-detectable cancers (n = 826, 57%). MRI-occult cancers constitute a prognostically distinct subgroup among higher-grade prostate cancers.

The role of MRI in prostate cancer: current and future directions.

There has been an increasing role of magnetic resonance imaging (MRI) in the management of prostate cancer. MRI already plays an essential role in the detection and staging, with the introduction of functional MRI sequences. Recent advancements in radiomics and artificial intelligence are being tested to potentially improve detection, assessment of aggressiveness, and provide usefulness as a prognostic marker. MRI can improve pretreatment risk stratification and therefore selection of and follow-up of patients for active surveillance. MRI can also assist in guiding targeted biopsy, treatment planning and follow-up after treatment to assess local recurrence. MRI has gained importance in the evaluation of metastatic disease with emerging technology including whole-body MRI and integrated positron emission tomography/MRI, allowing for not only better detection but also quantification. The main goal of this article is to review the most recent advances on MRI in prostate cancer and provide insights into its potential clinical roles from the radiologist's perspective. In each of the sections, specific roles of MRI tailored to each clinical setting are discussed along with its strengths and weakness including already established material related to MRI and the introduction of recent advancements on MRI.

Prognostic Utility of MRI Features in Intradiverticular Bladder Tumor.

BACKGROUND: Intradiverticular bladder tumors (IDBT) are rare but clinically important, as they are difficult to assess endoscopically due to limited anatomic access and risk of perforation. MRI may be helpful in assessing IDBT and providing relevant staging and prognostic information. PURPOSE: To assess MRI findings of IDBT and their relationship with overall survival. METHODS: This retrospective study included 31 consecutive patients with IDBT undergoing MRI from 2008 to 2018 identified through electronic medical records and PACS database search. Two radiologists independently assessed the following MRI features: size (>3 vs ≤3 cm), diverticular neck involvement, Vesical Imaging-Reporting and Data System (VI-RADS) score (>3 vs ≤3), perivesical fat infiltration, additional tumors and suspicious pelvic lymph nodes. Overall survival was estimated using Kaplan-Meier analysis; and the relationship with clinicopathological and MRI features was determined using the Cox proportional-hazards regression model. Inter-reader agreement was assessed using intraclass correlation coefficients (ICC) and Cohen's kappa (K). RESULTS: Median follow-up was 1044 days (interquartile range, 474-1952 days). Twenty-six (83.9%) patients underwent surgical treatment with or without neoadjuvant chemotherapy. On MRI, greater tumor size (>3 cm), diverticular neck involvement, perivesical extension, and suspicious lymph nodes were associated with lower overall survival (HR = 3.6-8.1 and 4.3-6.3 for the 2 radiologists, p ≤ 0.03). Other clinicopathological or MRI findings were not associated with survival (p = 0.27-0.65). Inter-reader agreement was excellent for tumor size (ICC = 0.991; 95% CI 0.982-0.996), fair for VI-RADS (K = 0.52, 95% CI, 0.22-0.82), and moderate for others (K = 0.61-0.79). CONCLUSION: In patients with IDBT, several MRI features were significantly associated with overall survival. Utilizing all available clinicopathological and imaging information may improve estimation of prognosis.

Correlation Between Imaging-Based Intermediate Endpoints and Overall Survival in Men With Metastatic Castration-Resistant Prostate Cancer: Analysis of 28 Randomized Trials Using the Prostate Cancer Clinical Trials Working Group (PCWG2) Criteria in 16,511 Patients.

INTRODUCTION/BACKGROUND: Radiographic progression-free survival (rPFS) based on Prostate Cancer Working Group 2 (PCWG2) has been increasingly used as a meaningful imaging-based intermediate endpoint (IBIE) for overall survival (OS) in patients with metastatic castration-resistant prostate cancer (mCRPC). In randomized phase III trials, rPFS showed good correlation with OS at the individual trial level. We aimed to assess the correlation between the hazard ratios (HR) of IBIE and OS among PCWG2-based randomized trials. MATERIALS AND METHODS: PubMed and EMBASE databases were systematically searched for randomized trials evaluating systemic treatments on mCRPC using PCWG2 up to April 15, 2020. Hazard ratios for OS and IBIEs were extracted and their correlation was assessed using weighted linear regression. Subgroup analyses were performed according to various clinical settings: prior chemotherapy, drug category, type of IBIE (rPFS vs. composite IBIE, latter defined as progression by imaging and one or a combination of PSA, pain, skeletal-related events, and performance status), and publication year. RESULTS: Twenty-eight phase II-III randomized trials (16,511 patients) were included. Correlation between OS and IBIE was good (R2 = 0.57, 95% confidence interval [CI], 0.35-0.78). Trials using rPFS showed substantially higher correlation than those using a composite IBIE (R2 = 0.58, 95% CI, 0.32-0.82 vs. 0.00, 95% CI, -0.01 to 0.01). Correlations between OS and IBIE in other subgroups were at least moderate in nearly all subgroups (R2 = 0.32-0.91). CONCLUSION: IBIEs in the era of PCWG2 correlate well with OS in randomized trials for systemic drugs in patients with mCRPC. PCWG2-based rPFS should be used instead of a composite IBIE that includes PSA and other clinical variables.

Local Extent of Prostate Cancer at MRI versus Prostatectomy Histopathology: Associations with Long-term Oncologic Outcomes.

Background It is unknown how the imperfect accuracy of MRI for local staging of prostate cancer relates to oncologic outcomes. Purpose To analyze how staging discordances between MRI and histopathologic evaluation relate to recurrence and survival after radical prostatectomy. Materials and Methods Health Insurance Portability and Accountability Act-compliant retrospective analysis of preprostatectomy T2-weighted prostate MRI (January 2001 to December 2006). Extraprostatic extension and seminal vesicle invasion were assessed by using five-point Likert scales; scores of 4 or higher were classified as positive. Biochemical recurrence (BCR), metastases, and prostate cancer-specific mortality rates were estimated with Kaplan-Meier and Cox models. Results A total of 2160 patients (median age, 60 years; interquartile range, 55-64 years) were evaluated. Among patients with histopathologic extraprostatic (pT3) disease (683 of 2160; 32%), those with organ-confined disease at MRI (384 of 683; 56%) experienced better outcomes than those with concordant extraprostatic disease at MRI and pathologic analysis: 15-year risk for BCR, 30% (95% CI: 22, 40) versus 68% (95% CI: 60, 75); risk for metastases, 14% (95% CI: 8.4, 24) versus 32% (95% CI: 26, 39); risk for prostate cancer-specific mortality, 3% (95% CI: 1, 6) versus 15% (95% CI: 9.5, 23) (P < .001 for all comparisons). Among patients with histopathologic organ-confined disease (pT2) (1477 of 2160; 68%), those with extraprostatic disease at MRI (102 of 1477; 7%) were at higher risk for BCR (27% [95% CI: 19, 37] vs 10% [95% CI: 8, 14]; P < .001), metastases (19% [95% CI: 6, 48] vs 3% [95% CI: 1, 6]; P < .001), and prostate cancer-specific mortality (2% [95% CI: 1, 9] vs 1% [95% CI: 0, 5]; P = .009) than those with concordant organ-confined disease at MRI and pathologic analysis. At multivariable analyses, tumor extent at MRI (hazard ratio range, 4.1-5.2) and histopathologic evaluation (hazard ratio range, 3.6-6.7) was associated with the risk for BCR, metastases, and prostate cancer-specific mortality (P < .001 for all analyses). Conclusion The local extent of prostate cancer at MRI is associated with oncologic outcomes after prostatectomy, independent of pathologic tumor stage. This might inform a strategy on how to integrate MRI into a clinical staging algorithm. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Gottlieb in this issue.

Impact of 18F-Fluorodeoxyglucose positron emission tomography on management of cancer of unknown primary: systematic review and meta-analysis.

BACKGROUND: Cancers of unknown primary (CUP) have traditionally been treated empirically, with a dismal prognosis. Compared with standard diagnostic tests, including CT and MRI, imaging with 18F-fluorodeoxyglucose (FDG) PET or PET/CT has shown the capacity to better identify the primary tumour site and detect additional sites of metastasis. However, its clinical impact is not well established. We performed a systematic review and meta-analysis of prior studies to assess the impact of FDG-PET or PET/CT on the management of patients with CUP. MATERIALS AND METHODS: Pubmed and EMBASE databases were searched up to 4th February 2021. Studies that reported the proportion of patients with CUP who experienced a management change after FDG-PET or PET/ computed tomography (CT) were included and the proportions were pooled using the random-effects model. Study quality was assessed using QUADAS-2. Subgroup analysis was conducted to explore heterogeneity. RESULTS: Thirty-eight studies (involving 2795 patients) were included. The pooled proportion of patients with management changes was 35% (95% confidence interval 31%-40%). There was substantial heterogeneity among the studies (Q-test, p < 0.01; I2 = 82%). The specific reason for management change was more commonly detection of the primary site (22% [95% CI 18-28%]) than detection of additional metastatic sites (14% [95% CI 10-19%]). The pooled proportions of patients with management changes were similar among numerous subgroups (range, 32.8%-38.2%). CONCLUSION: FDG-PET or PET/CT had a meaningful impact on the management of patients with CUP. Approximately, a third of patients had their management changed because of FDG-PET or PET/CT results, and this finding was consistent across numerous subgroups.

Emergency room imaging in pediatric patients with cancer: analysis of the spectrum and frequency of imaging modalities and findings in a tertiary cancer center and their relationship with survival.

BACKGROUND: To assess the spectrum and frequency of modalities used for emergency room (ER) imaging and their findings in pediatric cancer patients and assess their relationship with survival. METHODS: Consecutive pediatric cancer patients that underwent imaging during an ER visit at our tertiary cancer center over a 5-year period were retrospectively analyzed. Imaging findings were considered positive when they were relevant to the ER presenting complaint. Imaging positivity was correlated with inpatient admission. Overall survival (OS) was assessed with Kaplan-Meier curves and uni- and multi-variate Cox proportional hazards model was used to identify significant factors associated with OS. RESULTS: Two hundred sixty-one patients (135 males and 126 females; median age 11 years [interquartile range 5-16 years] with 348 visits and a total of 406 imaging studies were included. Common chief complaints were related to the chest (100 [28.7 %]) and fever (99 [28.4 %]). ER imaging was positive in 207 visits (59.5 %), commonly revealing increased metastases (50 [14.4 %]), pneumonia (47 [13.5 %]), and other lung problems (12 [2.9 %]). Positive ER imaging was associated with inpatient admission (69.3 % [133/192] vs. 40.4 % [63/156], p < 0.01). Multivariate survival analysis showed that positive ER imaging (hazard ratio [HR] = 2.35 [95% CI 1.44-3.83, p < 0.01), admission (HR = 1.86 [95% CI 1.17-3.00], p < 0.01), number of ER visits (HR = 3.08 [95% CI 1.62-5.83], p < 0.01 for ≥ 3 visits) were associated with poorer survival. CONCLUSIONS: Imaging was able to delineate the cause for ER visits in children with cancer in over half of the cases. Positive ER imaging was associated with admission and worse survival.

Patterns of Metastatic Disease in Patients with Cancer Derived from Natural Language Processing of Structured CT Radiology Reports over a 10-year Period.

Background Patterns of metastasis in cancer are increasingly relevant to prognostication and treatment planning but have historically been documented by means of autopsy series. Purpose To show the feasibility of using natural language processing (NLP) to gather accurate data from radiology reports for assessing spatial and temporal patterns of metastatic spread in a large patient cohort. Materials and Methods In this retrospective longitudinal study, consecutive patients who underwent CT from July 2009 to April 2019 and whose CT reports followed a departmental structured template were included. Three radiologists manually curated a sample of 2219 reports for the presence or absence of metastases across 13 organs; these manually curated reports were used to develop three NLP models with an 80%-20% split for training and test sets. A separate random sample of 448 manually curated reports was used for validation. Model performance was measured by accuracy, precision, and recall for each organ. The best-performing NLP model was used to generate a final database of metastatic disease across all patients. For each cancer type, statistical descriptive reports were provided by analyzing the frequencies of metastatic disease at the report and patient levels. Results In 91 665 patients (mean age ± standard deviation, 61 years ± 15; 46 939 women), 387 359 reports were labeled. The best-performing NLP model achieved accuracies from 90% to 99% across all organs. Metastases were most frequently reported in abdominopelvic (23.6% of all reports) and thoracic (17.6%) nodes, followed by lungs (14.7%), liver (13.7%), and bones (9.9%). Metastatic disease tropism is distinct among common cancers, with the most common first site being bones in prostate and breast cancers and liver among pancreatic and colorectal cancers. Conclusion Natural language processing may be applied to cancer patients' CT reports to generate a large database of metastatic phenotypes. Such a database could be combined with genomic studies and used to explore prognostic imaging phenotypes with relevance to treatment planning. © RSNA, 2021 Online supplemental material is available for this article.

The impact of scaling up access to treatment and imaging modalities on global disparities in breast cancer survival: a simulation-based analysis.

BACKGROUND: Female breast cancer is the most commonly diagnosed cancer in the world, with wide variations in reported survival by country. Women in low-income and middle-income countries (LMICs) in particular face several barriers to breast cancer services, including diagnostics and treatment. We aimed to estimate the potential impact of scaling up the availability of treatment and imaging modalities on breast cancer survival globally, together with improvements in quality of care. METHODS: For this simulation-based analysis, we used a microsimulation model of global cancer survival, which accounts for the availability and stage-specific survival impact of specific treatment modalities (chemotherapy, radiotherapy, surgery, and targeted therapy), imaging modalities (ultrasound, x-ray, CT, MRI, PET, and single-photon emission computed tomography [SPECT]), and quality of cancer care, to simulate 5-year net survival for women with newly diagnosed breast cancer in 200 countries and territories in 2018. We calibrated the model to empirical data on 5-year net breast cancer survival in 2010-14 from CONCORD-3. We evaluated the potential impact of scaling up specific imaging and treatment modalities and quality of care to the mean level of high-income countries, individually and in combination. We ran 1000 simulations for each policy intervention and report the means and 95% uncertainty intervals (UIs) for all model outcomes. FINDINGS: We estimate that global 5-year net survival for women diagnosed with breast cancer in 2018 was 67·9% (95% UI 62·9-73·4) overall, with an almost 25-times difference between low-income (3·5% [0·4-10·0]) and high-income (87·0% [85·6-88·4]) countries. Among individual treatment modalities, scaling up access to surgery alone was estimated to yield the largest survival gains globally (2·7% [95% UI 0·4-8·3]), and scaling up CT alone would have the largest global impact among imaging modalities (0·5% [0·0-2·0]). Scaling up a package of traditional modalities (surgery, chemotherapy, radiotherapy, ultrasound, and x-ray) could improve global 5-year net survival to 75·6% (95% UI 70·6-79·4), with survival in low-income countries improving from 3·5% (0·4-10·0) to 28·6% (4·9-60·1). Adding concurrent improvements in quality of care could further improve global 5-year net survival to 78·2% (95% UI 74·9-80·4), with a substantial impact in low-income countries, improving net survival to 55·3% (42·2-67·8). Comprehensive scale-up of access to all modalities and improvements in quality of care could improve global 5-year net survival to 82·3% (95% UI 79·3-85·0). INTERPRETATION: Comprehensive scale-up of treatment and imaging modalities, and improvements in quality of care could improve global 5-year net breast cancer survival by nearly 15 percentage points. Scale-up of traditional modalities and quality-of-care improvements could achieve 70% of these total potential gains, with substantial impact in LMICs, providing a more feasible pathway to improving breast cancer survival in these settings even without the benefits of future investments in targeted therapy and advanced imaging. FUNDING: Harvard T H Chan School of Public Health, and National Cancer Institute P30 Cancer Center Support Grant to Memorial Sloan Kettering Cancer Center.

International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer.

BACKGROUND: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data. METHODS: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings. RESULTS: Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models (p < 0.001 for all). CONCLUSIONS: In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models.

Oncologic Errors in Diagnostic Radiology: A 10-Year Analysis Based on Medical Malpractice Claims.

PURPOSE: To retrospectively analyze the nature and extent of oncology-related errors accounting for malpractice allegations in diagnostic radiology. METHODS: The Comparative Benchmarking System of the Controlled Risk Insurance Company, a database containing roughly 30% of medical malpractice claims in the United States, was searched retrospectively for the period 2008 to 2017. Claims naming radiology as a primary service were identified and were stratified and compared by oncologic versus nononcologic status, allegation type (diagnostic versus nondiagnostic), and imaging modality. RESULTS: Over the 10-year period, radiology was the primary responsible service for 3.9% of all malpractice claims (2,582 of 66,061) and 12.8% of claims with diagnostic allegations (1,756 of 13,695). Oncology (neoplasms) accounted for 44.0% of radiology cases with diagnostic allegations, a larger share than any other category of medical condition. Among radiology cases with diagnostic allegations, high-severity harm occurred in 79% of oncologic but just 42% of nononcologic cases. Of all oncologic radiology cases, 97.4% had diagnostic allegations, and just 55.0% of nononcologic radiology cases had diagnostic allegations. Imaging misinterpretation was a contributing factor for a large majority (80.7% [623 of 772]) of oncologic radiology cases with diagnostic allegations. The modalities most commonly used in oncologic radiology cases with diagnostic allegations involving misinterpretation were mammography and CT. CONCLUSION: Oncology represents the largest source of radiology malpractice cases with diagnostic allegations. Oncologic radiology malpractice cases are more likely than nononcologic radiology cases to be due to diagnostic errors. Furthermore, compared with those that are nononcologic, oncologic radiology cases with diagnostic allegations are more likely to be associated with high-severity harm. Efforts are warranted to reduce misinterpretations of oncologic imaging.

Medical imaging and nuclear medicine: a Lancet Oncology Commission.

The diagnosis and treatment of patients with cancer requires access to imaging to ensure accurate management decisions and optimal outcomes. Our global assessment of imaging and nuclear medicine resources identified substantial shortages in equipment and workforce, particularly in low-income and middle-income countries (LMICs). A microsimulation model of 11 cancers showed that the scale-up of imaging would avert 3·2% (2·46 million) of all 76·0 million deaths caused by the modelled cancers worldwide between 2020 and 2030, saving 54·92 million life-years. A comprehensive scale-up of imaging, treatment, and care quality would avert 9·55 million (12·5%) of all cancer deaths caused by the modelled cancers worldwide, saving 232·30 million life-years. Scale-up of imaging would cost US$6·84 billion in 2020-30 but yield lifetime productivity gains of $1·23 trillion worldwide, a net return of $179·19 per $1 invested. Combining the scale-up of imaging, treatment, and quality of care would provide a net benefit of $2·66 trillion and a net return of $12·43 per $1 invested. With the use of a conservative approach regarding human capital, the scale-up of imaging alone would provide a net benefit of $209·46 billion and net return of $31·61 per $1 invested. With comprehensive scale-up, the worldwide net benefit using the human capital approach is $340·42 billion and the return per dollar invested is $2·46. These improved health and economic outcomes hold true across all geographical regions. We propose actions and investments that would enhance access to imaging equipment, workforce capacity, digital technology, radiopharmaceuticals, and research and training programmes in LMICs, to produce massive health and economic benefits and reduce the burden of cancer globally.

Global costs, health benefits, and economic benefits of scaling up treatment and imaging modalities for survival of 11 cancers: a simulation-based analysis.

BACKGROUND: In addition to increased availability of treatment modalities, advanced imaging modalities are increasingly recommended to improve global cancer care. However, estimates of the costs and benefits of investments to improve cancer survival are scarce, especially for low-income and middle-income countries (LMICs). In this analysis, we aimed to estimate the costs and lifetime health and economic benefits of scaling up imaging and treatment modality packages on cancer survival, both globally and by country income group. METHODS: Using a previously developed model of global cancer survival, we estimated stage-specific cancer survival and life-years gained (accounting for competing mortality) in 200 countries and territories for patients diagnosed with one of 11 cancers (oesophagus, stomach, colon, rectum, anus, liver, pancreas, lung, breast, cervix uteri, and prostate) representing 60% of all cancer diagnoses between 2020 and 2030 (inclusive of full years). We evaluated the costs and health and economic benefits of scaling up packages of treatment (chemotherapy, surgery, radiotherapy, and targeted therapy), imaging modalities (ultrasound, x-ray, CT, MRI, PET, single-photon emission CT), and quality of care to the mean level of high-income countries, separately and in combination, compared with no scale-up. Costs and benefits are presented in 2018 US$ and discounted at 3% annually. FINDINGS: For the 11 cancers studied, we estimated that without scale-up (ie, with current availability of treatment, imaging, and quality of care) there will be 76·0 million cancer deaths (95% UI 73·9-78·6) globally for patients diagnosed between 2020 and 2030, with more than 70% of these deaths occurring in LMICs. Comprehensive scale-up of treatment, imaging, and quality of care could avert 12·5% (95% UI 9·0-16·3) of these deaths globally, ranging from 2·8% (1·8-4·3) in high-income countries to 38·2% (32·6-44·5) in low-income countries. Globally, we estimate that comprehensive scale-up would cost an additional $232·9 billion (95% UI 85·9-422·0) between 2020 and 2030 (representing a 6·9% increase in cancer treatment costs), but produce $2·9 trillion (1·8-4·0) in lifetime economic benefits, yielding a return of $12·43 (6·47-33·23) per dollar invested. Scaling up treatment and quality of care without imaging would yield a return of $6·15 (2·66-16·71) per dollar invested and avert 7·0% (3·9-10·3) of cancer deaths worldwide. INTERPRETATION: Simultaneous investment in cancer treatment, imaging, and quality of care could yield substantial health and economic benefits, especially in LMICs. These results provide a compelling rationale for the value of investing in the global scale-up of cancer care. FUNDING: Harvard TH Chan School of Public Health and National Cancer Institute.