Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial.

OBJECTIVES: Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI. METHODS: This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating. RESULTS: The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE). CONCLUSION: It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.

Re-attendance in supplemental breast MRI screening rounds of the DENSE trial for women with extremely dense breasts.

OBJECTIVES: Supplemental MRI screening improves early breast cancer detection and reduces interval cancers in women with extremely dense breasts in a cost-effective way. Recently, the European Society of Breast Imaging recommended offering MRI screening to women with extremely dense breasts, but the debate on whether to implement it in breast cancer screening programs is ongoing. Insight into the participant experience and willingness to re-attend is important for this discussion. METHODS: We calculated the re-attendance rates of the second and third MRI screening rounds of the DENSE trial. Moreover, we calculated age-adjusted odds ratios (ORs) to study the association between characteristics and re-attendance. Women who discontinued MRI screening were asked to provide one or more reasons for this. RESULTS: The re-attendance rates were 81.3% (3458/4252) and 85.2% (2693/3160) in the second and third MRI screening round, respectively. A high age (> 65 years), a very low BMI, lower education, not being employed, smoking, and no alcohol consumption were correlated with lower re-attendance rates. Moderate or high levels of pain, discomfort, or anxiety experienced during the previous MRI screening round were correlated with lower re-attendance rates. Finally, a plurality of women mentioned an examination-related inconvenience as a reason to discontinue screening (39.1% and 34.8% in the second and third screening round, respectively). CONCLUSIONS: The willingness of women with dense breasts to re-attend an ongoing MRI screening study is high. However, emphasis should be placed on improving the MRI experience to increase the re-attendance rate if widespread supplemental MRI screening is implemented. CLINICAL RELEVANCE STATEMENT: For many women, MRI is an acceptable screening method, as re-attendance rates were high - even for screening in a clinical trial setting. To further enhance the (re-)attendance rate, one possible approach could be improving the overall MRI experience. KEY POINTS: • The willingness to re-attend in an ongoing MRI screening study is high. • Pain, discomfort, and anxiety in the previous MRI screening round were related to lower re-attendance rates. • Emphasis should be placed on improving MRI experience to increase the re-attendance rate in supplemental MRI screening.

External validation of the RSNA 2020 pulmonary embolism detection challenge winning deep learning algorithm.

PURPOSE: To evaluate the diagnostic performance and generalizability of the winning DL algorithm of the RSNA 2020 PE detection challenge to a local population using CTPA data from two hospitals. MATERIALS AND METHODS: Consecutive CTPA images from patients referred for suspected PE were retrospectively analysed. The winning RSNA 2020 DL algorithm was retrained on the RSNA-STR Pulmonary Embolism CT (RSPECT) dataset. The algorithm was tested in hospital A on multidetector CT (MDCT) images of 238 patients and in hospital B on spectral detector CT (SDCT) and virtual monochromatic images (VMI) of 114 patients. The output of the DL algorithm was compared with a reference standard, which included a consensus reading by at least two experienced cardiothoracic radiologists for both hospitals. Areas under the receiver operating characteristic curve (AUCs) were calculated. Sensitivity and specificity were determined using the maximum Youden index. RESULTS: According to the reference standard, PE was present in 73 patients (30.7%) in hospital A and 33 patients (29.0%) in hospital B. For the DL algorithm the AUC was 0.96 (95% CI 0.92-0.98) in hospital A, 0.89 (95% CI 0.81-0.94) for conventional reconstruction in hospital B and 0.87 (95% CI 0.80-0.93) for VMI. CONCLUSION: The RSNA 2020 pulmonary embolism detection on CTPA challenge winning DL algorithm, retrained on the RSPECT dataset, showed high diagnostic accuracy on MDCT images. A somewhat lower performance was observed on SDCT images, which suggest additional training on novel CT technology may improve generalizability of this DL algorithm.

The value of chest radiography after chest tube removal in nonventilated trauma patients: A post hoc analysis of a multicenter prospective cohort study.

BACKGROUND: Chest tubes are commonly placed in trauma care to treat life-threatening intrathoracic injuries by evacuating blood or air from the pleural cavity. Currently, it is common practice to routinely obtain chest radiographs between 1 to 8 hours after chest tube removal, while the necessity of it has been questioned. This study describes the "ins-and-outs" of chest tubes and evaluates the value of routine postremoval chest radiography in nonventilated trauma patients. METHODS: A post hoc analysis of a multicenter observational prospective cohort study was performed in blunt chest trauma patients admitted with multiple rib fractures to two level 1 trauma centers between January 2018 and March 2021 and treated with one or more chest tubes. Exclusion criteria were mechanical ventilation during chest tube removal, missing reports of postremoval chest radiography, transfer to another hospital, or mortality before chest tube removal. Descriptive analyses were performed to calculate the number of findings on postremoval chest radiographs and reinterventions. RESULTS: A total of 207 patients were included for analysis of whom 14 underwent bilateral chest tube placement, resulting in 221 chest tube removals investigated in this study. The mean ± SD age was 58 ± 17 years, 71% were male, 73% had American Society of Anesthesiologists scores of 1 or 2, and the median Injury Severity Score was 19 (interquartile range, 14-29). In 68 of 221 chest tube removals (31%), postremoval chest radiography showed increased or recurrent intrathoracic pathology (i.e., 13% pneumothorax, 18% pleural fluid, and 8% atelectasis). Only two (3%) of these patients underwent a same-day reintervention based on these findings, of whom one had signs or symptoms of recurrent pathology and one was asymptomatic. CONCLUSION: It seems safe to omit routine use of postremoval chest radiography in nonventilated blunt chest trauma patients and to selectively use imaging in those patients presenting with clinical signs or symptoms after chest tube removal. LEVEL OF EVIDENCE: Diagnostic Tests/Criteria; Level IV.

Added value of an artificial intelligence algorithm in reducing the number of missed incidental acute pulmonary embolism in routine portal venous phase chest CT.

OBJECTIVES: The purpose of this study was to evaluate the incremental value of artificial intelligence (AI) compared to the diagnostic accuracy of radiologists alone in detecting incidental acute pulmonary embolism (PE) on routine portal venous contrast-enhanced chest computed tomography (CT). METHODS: CTs of 3089 consecutive patients referred to the radiology department for a routine contrast-enhanced chest CT between 27-5-2020 and 31-12-2020, were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The diagnostic performance of the AI was compared to the initial report. To determine the reference standard, discordant findings were independently evaluated by two readers. In case of disagreement, another experienced cardiothoracic radiologist with knowledge of the initial report and the AI output adjudicated. RESULTS: The prevalence of acute incidental PE in the reference standard was 2.2% (67 of 3089 patients). In 25 cases, AI detected initially unreported PE. This included three cases concerning central/lobar PE. Sensitivity of the AI algorithm was significantly higher than the outcome of the initial report (respectively 95.5% vs. 62.7%, p < 0.001), whereas specificity was very high for both (respectively 99.6% vs 99.9%, p = 0.012). The AI algorithm only showed a slightly higher amount of false-positive findings (11 vs. 2), resulting in a significantly lower PPV (85.3% vs. 95.5%, p = 0.047). CONCLUSION: The AI algorithm showed high diagnostic accuracy in diagnosing incidental PE, detecting an additional 25 cases of initially unreported PE, accounting for 37.3% of all positive cases. CLINICAL RELEVANCE STATEMENT: Radiologist support from AI algorithms in daily practice can prevent missed incidental acute PE on routine chest CT, without a high burden of false-positive cases. KEY POINTS: • Incidental pulmonary embolism is often missed by radiologists in non-diagnostic scans with suboptimal contrast opacification within the pulmonary trunk. • An artificial intelligence algorithm showed higher sensitivity detecting incidental pulmonary embolism on routine portal venous chest CT compared to the initial report. • Implementation of artificial intelligence support in routine daily practice will reduce the number of missed incidental pulmonary embolism.

An artificial intelligence algorithm for pulmonary embolism detection on polychromatic computed tomography: performance on virtual monochromatic images.

OBJECTIVES: Virtual monochromatic images (VMI) are increasingly used in clinical practice as they improve contrast-to-noise ratio. However, due to their different appearances, the performance of artificial intelligence (AI) trained on conventional CT images may worsen. The goal of this study was to assess the performance of an established AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPI) to detect pulmonary embolism (PE) on VMI. METHODS: Paired 60 kiloelectron volt (keV) VMI and CPI of 114 consecutive patients suspected of PE, obtained with a detector-based spectral CT scanner, were retrospectively analyzed by an established AI algorithm. The CT pulmonary angiography (CTPA) were classified as positive or negative for PE on a per-patient level. The reference standard was established using a comprehensive method that combined the evaluation of the attending radiologist and three experienced cardiothoracic radiologists aided by two different detection tools. Sensitivity, specificity, positive and negative predictive values and likelihood ratios of the algorithm on VMI and CPI were compared. RESULTS: The prevalence of PE according to the reference standard was 35.1% (40 patients). None of the diagnostic accuracy measures of the algorithm showed a significant difference between CPI and VMI. Sensitivity was 77.5% (95% confidence interval (CI) 64.6-90.4%) and 85.0% (73.9-96.1%) (p = 0.08) on CPI and VMI respectively and specificity 96.0% (91.4-100.0%) and 94.6% (89.4-99.7%) (p = 0.32). CONCLUSIONS: Diagnostic performance of the AI algorithm that was trained on CPI did not drop on VMI, which is reassuring for its use in clinical practice. CLINICAL RELEVANCE STATEMENT: A commercially available AI algorithm, trained on conventional polychromatic CTPA, could be safely used on virtual monochromatic images. This supports the sustainability of AI-aided detection of PE on CT despite ongoing technological advances in medical imaging, although monitoring in daily practice will remain important. KEY POINTS: • Diagnostic accuracy of an AI algorithm trained on conventional polychromatic images to detect PE did not drop on virtual monochromatic images. • Our results are reassuring as innovations in hardware and reconstruction in CT are continuing, whilst commercial AI algorithms that are trained on older generation data enter healthcare.

Assessing Quantitative Parenchymal Features at Baseline Dynamic Contrast-enhanced MRI and Cancer Occurrence in Women with Extremely Dense Breasts.

Background Automated identification of quantitative breast parenchymal enhancement features on dynamic contrast-enhanced (DCE) MRI scans could provide added value in assessment of breast cancer risk in women with extremely dense breasts. Purpose To automatically identify quantitative properties of the breast parenchyma on baseline DCE MRI scans and assess their association with breast cancer occurrence in women with extremely dense breasts. Materials and Methods This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. MRI was performed in eight hospitals between December 2011 and January 2016. After segmentation of fibroglandular tissue, quantitative features (including volumetric density, volumetric morphology, and enhancement characteristics) of the parenchyma were extracted from baseline MRI scans. Principal component analysis was used to identify parenchymal measures with the greatest variance. Multivariable Cox proportional hazards regression was applied to assess the association between breast cancer occurrence and quantitative parenchymal features, followed by stratification of significant features into tertiles. Results A total of 4553 women (mean age, 55.7 years ± 6 [SD]) with extremely dense breasts were included; of these women, 122 (3%) were diagnosed with breast cancer. Five principal components representing 96% of the variance were identified, and the component explaining the greatest independent variance (42%) consisted of MRI features relating to volume of enhancing parenchyma. Multivariable analysis showed that volume of enhancing parenchyma was associated with breast cancer occurrence (hazard ratio [HR], 1.09; 95% CI: 1.01, 1.18; P = .02). Additionally, women in the high tertile of volume of enhancing parenchyma showed a breast cancer occurrence twice that of women in the low tertile (HR, 2.09; 95% CI: 1.25, 3.61; P = .005). Conclusion In women with extremely dense breasts, a high volume of enhancing parenchyma on baseline DCE MRI scans was associated with increased occurrence of breast cancer as compared with a low volume of enhancing parenchyma. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Grimm in this issue.

Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study.

BACKGROUND: Body composition during childhood may predispose to negative health outcomes later in life. Automatic segmentation may assist in quantifying pediatric body composition in children. OBJECTIVE: To evaluate automatic segmentation for body composition on pediatric computed tomography (CT) scans and to provide normative data on muscle and fat areas throughout childhood using automatic segmentation. MATERIALS AND METHODS: In this pilot study, 537 children (ages 1-17 years) who underwent abdominal CT after high-energy trauma at a Dutch tertiary center (2002-2019) were retrospectively identified. Of these, the CT images of 493 children (66% boys) were used to establish normative data. Muscle (psoas, paraspinal and abdominal wall) and fat (subcutaneous and visceral) areas were measured at the third lumbar vertebral (L3) level by automatic segmentation. A representative subset of 52 scans was also manually segmented to evaluate the performance of automatic segmentation. RESULTS: For manually-segmented versus automatically-segmented areas (52 scans), mean Dice coefficients were high for muscle (0.87-0.90) and subcutaneous fat (0.88), but lower for visceral fat (0.60). In the control group, muscle area was comparable for both sexes until the age of 13 years, whereafter, boys developed relatively more muscle. From a young age, boys were more prone to visceral fat storage than girls. Overall, boys had significantly higher visceral-to-subcutaneous fat ratios (median 1.1 vs. 0.6, P<0.01) and girls higher fat-to-muscle ratios (median 1.0 vs. 0.7, P<0.01). CONCLUSION: Automatic segmentation of L3-level muscle and fat areas allows for accurate quantification of pediatric body composition. Using automatic segmentation, the development in muscle and fat distribution during childhood (in otherwise healthy) Dutch children was demonstrated.

Retrospective batch analysis to evaluate the diagnostic accuracy of a clinically deployed AI algorithm for the detection of acute pulmonary embolism on CTPA.

PURPOSE: To generate and extend the evidence on the clinical validity of an artificial intelligence (AI) algorithm to detect acute pulmonary embolism (PE) on CT pulmonary angiography (CTPA) of patients suspected of PE and to evaluate the possibility of reducing the risk of missed findings in clinical practice with AI-assisted reporting. METHODS: Consecutive CTPA scan data of 3316 patients referred because of suspected PE between 24-2-2018 and 31-12-2020 were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The output of the AI was compared with the attending radiologists' report. To define the reference standard, discordant findings were independently evaluated by two readers. In case of disagreement, an experienced cardiothoracic radiologist adjudicated. RESULTS: According to the reference standard, PE was present in 717 patients (21.6%). PE was missed by the AI in 23 patients, while the attending radiologist missed 60 PE. The AI detected 2 false positives and the attending radiologist 9. The sensitivity for the detection of PE by the AI algorithm was significantly higher compared to the radiology report (96.8% vs. 91.6%, p < 0.001). Specificity of the AI was also significantly higher (99.9% vs. 99.7%, p = 0.035). NPV and PPV of the AI were also significantly higher than the radiology report. CONCLUSION: The AI algorithm showed a significantly higher diagnostic accuracy for the detection of PE on CTPA compared to the report of the attending radiologist. This finding indicates that missed positive findings could be prevented with the implementation of AI-assisted reporting in daily clinical practice. CRITICAL RELEVANCE STATEMENT: Missed positive findings on CTPA of patients suspected of pulmonary embolism can be prevented with the implementation of AI-assisted care. KEY POINTS: The AI algorithm showed excellent diagnostic accuracy detecting PE on CTPA. Accuracy of the AI was significantly higher compared to the attending radiologist. Highest diagnostic accuracy can likely be achieved by radiologists supported by AI. Our results indicate that implementation of AI-assisted reporting could reduce the number of missed positive findings.

Optimizing carboplatin dosing by an improved prediction of carboplatin clearance using a CT-enhanced estimate of renal function.

AIMS: Carboplatin is generally dosed based on a modified Calvert formula, in which the Cockcroft-Gault-based creatinine clearance (CRCL) is used as proxy for the glomerular filtration rate (GFR). The Cockcroft-Gault formula (CG) overpredicts CRCL in patients with an aberrant body composition. The CT-enhanced estimate of RenAl FuncTion (CRAFT) was developed to compensate for this overprediction. We aimed to evaluate whether carboplatin clearance is better predicted by CRCL based on the CRAFT compared to the CG. METHODS: Data of four previously conducted trials was used. The CRAFT was divided by serum creatinine to derive CRCL. The difference between CRAFT- and CG-based CRCL was assessed by population pharmacokinetic modelling. Furthermore, the difference in calculated carboplatin dose was assessed in a heterogeneous dataset. RESULTS: In total, 108 patients were included in the analysis. Addition of the CRAFT- and CG-based CRCL as covariate on carboplatin clearance led, respectively, to an improved model fit with a 26-point drop in objective function value and a worsened model fit with an increase of 8 points. In 19 subjects with serum creatinine <50 µmol/L, the calculated carboplatin dose was 233 mg higher using the CG. CONCLUSIONS: Carboplatin clearance is better predicted by CRAFT vs. CG-based CRCL. In subjects with low serum creatinine, the calculated carboplatin dose using CG exceeds the dose using CRAFT, which might explain the need for dose capping when using the CG. Therefore, the CRAFT might be an alternative for dose capping while still dosing accurately.

The association of radiologic body composition parameters with clinical outcomes in level-1 trauma patients.

PURPOSE: The present study aims to assess whether CT-derived muscle mass, muscle density, and visceral fat mass are associated with in-hospital complications and clinical outcome in level-1 trauma patients. METHODS: A retrospective cohort study was conducted on adult patients admitted to the University Medical Center Utrecht following a trauma between January 1 and December 31, 2017. Trauma patients aged 16 years or older without severe neurological injuries, who underwent a CT that included the abdomen within 7 days of admission, were included. An artificial intelligence (AI) algorithm was used to retrieve muscle areas to calculate the psoas muscle index and to retrieve psoas muscle radiation attenuation and visceral fat (VF) area from axial CT images. Multivariable logistic and linear regression analyses were performed to assess associations between body composition parameters and outcomes. RESULTS: A total of 404 patients were included for analysis. The median age was 49 years (interquartile range [IQR] 30-64), and 66.6% were male. Severe comorbidities (ASA 3-4) were seen in 10.9%, and the median ISS was 9 (IQR 5-14). Psoas muscle index was not independently associated with complications, but it was associated with ICU admission (odds ratio [OR] 0.79, 95% confidence interval [CI] 0.65-0.95), and an unfavorable Glasgow Outcome Scale (GOS) score at discharge (OR 0.62, 95% CI 0.45-0.85). Psoas muscle radiation attenuation was independently associated with the development of any complication (OR 0.60, 95% CI 0.42-0.85), pneumonia (OR 0.63, 95% CI 0.41-0.96), and delirium (OR 0.49, 95% CI 0.28-0.87). VF was associated with developing a delirium (OR 1.95, 95% CI 1.12-3.41). CONCLUSION: In level-1 trauma patients without severe neurological injuries, automatically derived body composition parameters are able to independently predict an increased risk of specific complications and other poor outcomes.

Cardiovascular parameters on computed tomography are independently associated with in-hospital complications and outcomes in level-1 trauma patients.

BACKGROUND: In-hospital complications after trauma may result in prolonged stays, higher costs, and adverse functional outcomes. Among reported risk factors for complications are pre-existing cardiopulmonary comorbidities. Objective and quick evaluation of cardiovascular risk would be beneficial for risk assessment in trauma patients. Studies in non-trauma patients suggested an independent association between cardiovascular abnormalities visible on routine computed tomography (CT) imaging and outcomes. However, whether this applies to trauma patients is unknown. PURPOSE: To assess the association between cardiopulmonary abnormalities visible on routine CT images and the development of in-hospital complications in patients in a level-1 trauma center. METHODS: All trauma patients aged 16 years or older with CT imaging of the abdomen, thorax, or spine and admitted to the UMC Utrecht in 2017 were included. Patients with an active infection upon admission or severe neurological trauma were excluded. Routine trauma CT images were analyzed for visible abnormalities: pulmonary emphysema, coronary artery calcifications, and abdominal aorta calcification severity. Drug-treated complications were scored. The discharge condition was measured on the Glasgow Outcome Scale. RESULTS: In total, 433 patients (median age 50 years, 67% male, 89% ASA 1-2) were analyzed. Median Injury Severity Score and Glasgow Coma Scale score were 9 and 15, respectively. Seventy-six patients suffered from at least one complication, mostly pneumonia (n = 39, 9%) or delirium (n = 19, 4%). Left main coronary artery calcification was independently associated with the development of any complication (OR 3.9, 95% CI 1.7-8.9). An increasing number of calcified coronary arteries showed a trend toward an association with complications (p = 0.07) and was significantly associated with an adverse discharge condition (p = 0.02). Pulmonary emphysema and aortic calcifications were not associated with complications. CONCLUSION: Coronary artery calcification, visible on routine CT imaging, is independently associated with in-hospital complications and an adverse discharge condition in level-1 trauma patients. The findings of this study may help to identify trauma patients quickly and objectively at risk for complications in an early stage without performing additional diagnostics or interventions.

Computed tomographic bone mineral density is independently associated with adverse in-hospital outcomes in Dutch level-1 trauma patients.

BACKGROUND: Aging, inactivity, and malnutrition are risk factors for adverse in-hospital outcomes and can manifest in bone loss. Use of bone mineral density (BMD) as an objective marker might improve early identification of patients at risk for complications. AIM: To assess the association of computed tomography (CT) determined BMD values of the first lumbar vertebra with in-hospital complications and outcomes in trauma patients. METHODS: All consecutive hospitalized trauma patients (≥ 16 years) that underwent CT-imaging within 7 days of admission in 2017 were included. Patients with an active infection or antibiotic treatment upon admission, severe neurologic trauma, or an unassessable vertebra were excluded. BMD at the first lumbar vertebra was determined with CT by placing a circular region of interest in homogeneous trabecular bone to obtain mean Hounsfield Units (HU). Regression analyses were performed to assess the association of BMD with in-hospital complications and outcomes. RESULTS: In total, 410 patients were included (median age: 49 years [interquartile range 30-64], 68.3% men, mean BMD 159 ± 66 HU). A total of 94 complications, primarily infection-related, were registered in 74 patients. After adjustment for covariates, a decrease of BMD by one standard deviation was significantly associated with increased risk of complications (odds ratio [OR] 1.9, 95% confidence interval [CI] 1.1-3.1), pneumonia (OR 2.2, 95% CI 1.2-4.5), delirium (OR 4.5, 95% CI 1.7-13.5), and intensive care unit (ICU) admission (OR 1.8, 95% CI 1.1-2.9). CONCLUSION: Bone mineral density of the first lumbar vertebra is independently associated with in-hospital complications, pneumonia, delirium, and ICU admission. These findings could help identify patients at risk early.

Challenges and Changes of the Breast Cancer Screening Paradigm.

Since four decades mammography is used for early breast cancer detection in asymptomatic women and still remains the gold standard imaging modality. However, population screening programs can be personalized and women can be divided into different groups based on risk factors and personal preferences. The availability of new and evolving imaging modalities, for example, digital breast tomosynthesis, dynamic-contrast-enhanced magnetic resonance imaging (MRI), abbreviated MRI protocols, diffusion-weighted MRI, and contrast-enhanced mammography leads to new challenges and perspectives regarding the feasibility and potential harms of breast cancer screening. The aim of this review is to discuss the current guidelines for different risk groups, to analyze the recent published studies about the diagnostic performance of the imaging modalities and to discuss new developments and future perspectives. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 6.

Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial.

OBJECTIVES: Computer-aided triaging (CAT) and computer-aided diagnosis (CAD) of screening breast magnetic resonance imaging have shown potential to reduce the workload of radiologists in the context of dismissing normal breast scans and dismissing benign disease in women with extremely dense breasts. The aim of this study was to validate the potential of integrating CAT and CAD to reduce workload and workup on benign lesions in the second screening round of the DENSE trial, without missing cancer. METHODS: We included 2901 breast magnetic resonance imaging scans, obtained from 8 hospitals in the Netherlands. Computer-aided triaging and CAD were previously developed on data from the first screening round. Computer-aided triaging dismissed examinations without lesions. Magnetic resonance imaging examinations triaged to radiological reading were counted and subsequently processed by CAD. The number of benign lesions correctly classified by CAD was recorded. The false-positive fraction of the CAD was compared with that of unassisted radiological reading in the second screening round. Receiver operating characteristics (ROC) analysis was performed and the generalizability of CAT and CAD was assessed by comparing results from first and second screening rounds. RESULTS: Computer-aided triaging dismissed 950 of 2901 (32.7%) examinations with 49 lesions in total; none were malignant. Subsequent CAD classified 132 of 285 (46.3%) lesions as benign without misclassifying any malignant lesion. Together, CAT and CAD yielded significantly fewer false-positive lesions, 53 of 109 (48.6%) and 89 of 109 (78.9%), respectively ( P = 0.001), than radiological reading alone. Computer-aided triaging had a smaller area under the ROC curve in the second screening round compared with the first, 0.83 versus 0.76 ( P = 0.001), but this did not affect the negative predictive value at the 100% sensitivity operating threshold. Computer-aided diagnosis was not associated with significant differences in area under the ROC curve (0.857 vs 0.753, P = 0.08). At the operating thresholds, the specificities of CAT (39.7% vs 41.0%, P = 0.70) and CAD (41.0% vs 38.2%, P = 0.62) were successfully reproduced in the second round. CONCLUSION: The combined application of CAT and CAD showed potential to reduce workload of radiologists and to reduce number of biopsies on benign lesions. Computer-aided triaging (CAT) correctly dismissed 950 of 2901 (32.7%) examinations with 49 lesions in total; none were malignant. Subsequent computer-aided diagnosis (CAD) classified 132 of 285 (46.3%) lesions as benign without misclassifying any malignant lesion. Together, CAT and CAD yielded significantly fewer false-positive lesions, 53 of 109 (48.6%) and 89 of 109 (78.9%), respectively ( P = 0.001), than radiological reading alone.

Fluorescent Indocyanine Green versus Technetium-99m and Blue Dye for Bilateral SENTinel Lymph Node Detection in Stage I-IIA Cervical Cancer (FluoreSENT): protocol for a non-inferiority study.

INTRODUCTION: Nowadays, two predominant methods for detecting sentinel lymph nodes (SLNs) in cervical cancer are in use. The most conventional method is a combination of a radiotracer, technetium-99m (99mTc) and blue dye. More recently, another method for SLN mapping using indocyanine green (ICG) is becoming widely accepted. ICG is a fluorescent dye, visualised intraoperatively with near-infrared (NIR) fluorescence imaging, providing real-time visual navigation. The presumed advantages of ICG over 99mTc, that is, being cheaper, non-radioactive and logistically more attractive, are only valuable if its detection rate proves to be at least non-inferior. Before omitting the well-functioning and evidence-based combined approach of 99mTc and blue dye, we aim to provide prospective evidence on the non-inferiority of ICG with NIR fluorescence imaging. METHODS AND ANALYSIS: We initiated a prospective non-inferiority study with a paired comparison of both SLN methods in a single sample of 101 patients with International Federation of Gynecology and Obstetrics (FIGO) stage IA-IB2 or IIA1 cervical cancer receiving primary surgical treatment. All patients undergo SLN mapping with ICG and NIR fluorescence imaging in adjunct to mapping with 99mTc (including single photon emission computed tomography with X-ray computed tomography (SPECT/CT)) and blue dye. Surgeons start SLN detection with ICG while being blinded for the preoperative outcome of SPECT/CT to avoid biased detection with ICG. Primary endpoint of this study is bilateral SLN detection rate of both methods (ie, detection of at least one SLN in each hemipelvis). Since we compare strategies for SLN mapping that are already applied in current daily practice for different types of cancer, no additional risks or burdens are expected from these study procedures. ETHICS AND DISSEMINATION: The current study is approved by the Medical Ethics Research Committee Utrecht (reference number 21-014). Findings arising from this study will be disseminated in peer-reviewed journals, academic conferences and through patient organisations. TRIAL REGISTRATION NUMBER: NL9011 and EudraCT 2020-005134-15.

Exposure-Response Analysis of Osimertinib in EGFR Mutation Positive Non-Small Cell Lung Cancer Patients in a Real-Life Setting.

BACKGROUND: Osimertinib, an irreversible inhibitor of the epidermal growth factor receptor (EGFR) is an important drug in the treatment of EGFR-mutation positive non-small cell lung cancer (NSCLC). Clinical trials with osimertinib could not demonstrate an exposure-efficacy relationship, while a relationship between exposure and toxicity has been found. In this study, we report the exposure-response relationships of osimertinib in a real-life setting. METHODS: A retrospective observational cohort study was performed, including patients receiving 40 - 80 mg osimertinib as ≥ 2 line therapy and from whom pharmacokinetic samples were collected during routine care. Trough plasma concentrations (Cmin,pred) were estimated and used as a measure of osimertinib exposure. A previously defined exploratory pharmacokinetic threshold of 166 µg/L was taken to explore the exposure-efficacy relationship. RESULTS: A total of 145 patients and 513 osimertinib plasma concentration samples were included. Median progression free survival (PFS) was 13.3 (95% confidence interval (CI):10.3 - 19.1) months and 9.3 (95% CI: 7.2 - 11.1) months for patients with Cmin,pred < 166 µg/L and Cmin,pred ≥ 166 µg/L, respectively (p = 0.03). In the multivariate analysis, a Cmin,pred < 166 µg/L resulted in a non-statistically significant hazard ratio of 1.10 (95% CI: 0.60 - 2.01; p = 77). Presence of a EGFR driver-mutation other than the exon 19 del or L858R mutations, led to a shorter PFS with a hazard ratio of 2.89 (95% CI: 1.18 - 7.08; p = 0.02). No relationship between exposure and toxicity was observed (p = 0.91). CONCLUSION: In our real-life cohort, no exposure-response relationship was observed for osimertinib in the current dosing scheme. The feasibility of a standard lower fixed dosing of osimertinib in clinical practice should be studied prospectively.

Individualized Contrast Media Application Based on Body Weight and Contrast Enhancement in Computed Tomography of Livers without Steatosis.

This study analyzes the homogeneity in liver attenuation of a body-weight-based protocol compared to a semi-fixed protocol. Patients undergoing abdominal multiphase computed tomography received 0.500 g of iodine (gI) per kilogram of body weight. Liver attenuation and enhancement were determined using regions of interest on scans in the pre-contrast and portal venous phases. The outcomes were analyzed for interpatient uniformity in weight groups. The subjective image quality was scored using a four-point Likert scale (excellent, good, moderate, and nondiagnostic). A total of 80 patients were included (56.3% male, 64 years, 78.0 kg) and were compared to 80 propensity-score-matched patients (62.5% male, 63 years, 81.7 kg). The liver attenuation values for different weight groups of the TBW-based protocol were not significantly different (p = 0.331): 109.1 ± 13.8 HU (≤70 kg), 104.6 ± 9.70 HU (70−90 kg), and 105.1 ± 11.6 HU (≥90 kg). For the semi-fixed protocol, there was a significant difference between the weight groups (p < 0.001): 121.1 ± 12.1 HU (≤70 kg), 108.9 ± 11.0 HU (70−90 kg), and 105.0 ± 9.8 HU (≥90 kg). For the TBW-based protocol, the enhancement was not significantly different between the weight groups (p = 0.064): 46.2 ± 15.1 HU (≤70 kg), 59.3 ± 6.8 HU (70−90 kg), and 52.1 ± 11.7 HU (≥90 kg). Additionally, for the semi-fixed protocol, the enhancement was not significantly different between the weight groups (p = 0.069): 59.4 ± 11.0 HU (≤70 kg), 53.0 ± 10.3 HU (70−90 kg), and 52.4 ± 7.5 HU (≥90 kg). The mean administered amount of iodine per kilogram was less for the TBW-based protocol compared to the semi-fixed protocol: 0.499 ± 0.012 and 0.528 ± 0.079, respectively (p = 0.002). Of the TBW-based protocol, 17.5% of the scans scored excellent enhancement quality, 76.3% good, and 6.3% moderate. Of the semi-fixed protocol, 70.0% scored excellent quality, 21.3% scored good, and 8.8% scored moderate. In conclusion, the TBW-based protocol increased the interpatient uniformity of liver attenuation but not the enhancement in the portal venous phase compared to the semi-fixed protocol, using an overall lower amount of contrast media and maintaining good subjective image quality.

Dynamic Contrast-enhanced and Diffusion-weighted Magnetic Resonance Imaging for Response Evaluation After Single-Dose Ablative Neoadjuvant Partial Breast Irradiation.

Purpose: We aimed to evaluate changes in dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) magnetic resonance imaging (MRI) scans acquired before and after single-dose ablative neoadjuvant partial breast irradiation (NA-PBI), and explore the relation between semiquantitative MRI parameters and radiologic and pathologic responses. Methods and Materials: We analyzed 3.0T DCE and DW-MRI of 36 patients with low-risk breast cancer who were treated with single-dose NA-PBI, followed by breast-conserving surgery 6 or 8 months later. MRI was acquired before NA-PBI and 1 week, 2, 4, and 6 months after NA-PBI. Breast radiologists assessed the radiologic response and breast pathologists scored the pathologic response after surgery. Patients were grouped as either pathologic responders or nonresponders (<10% vs ≥10% residual tumor cells). The semiquantitative MRI parameters evaluated were time to enhancement (TTE), 1-minute relative enhancement (RE1min), percentage of enhancing voxels (%EV), distribution of washout curve types, and apparent diffusion coefficient (ADC). Results: In general, the enhancement increased 1 week after NA-PBI (baseline vs 1 week median - TTE: 15s vs 10s; RE1min: 161% vs 197%; %EV: 47% vs 67%) and decreased from 2 months onward (6 months median - TTE: 25s; RE1min: 86%; %EV: 12%). Median ADC increased from 0.83 × 10-3 mm2/s at baseline to 1.28 × 10-3 mm2/s at 6 months. TTE, RE1min, and %EV showed the most potential to differentiate between radiologic responses, and TTE, RE1min, and ADC between pathologic responses. Conclusions: Semiquantitative analyses of DCE and DW-MRI showed changes in relative enhancement and ADC 1 week after NA-PBI, indicating acute inflammation, followed by changes indicating tumor regression from 2 to 6 months after radiation therapy. A relation between the MRI parameters and radiologic and pathologic responses could not be proven in this exploratory study.