Development of a Radiomics-Based Model to Predict Graft Fibrosis in Liver Transplant Recipients: A Pilot Study.

Liver Transplantation is complicated by recurrent fibrosis in 40% of recipients. We evaluated the ability of clinical and radiomic features to flag patients at risk of developing future graft fibrosis. CT scans of 254 patients at 3-6 months post-liver transplant were retrospectively analyzed. Volumetric radiomic features were extracted from the portal phase using an Artificial Intelligence-based tool (PyRadiomics). The primary endpoint was clinically significant (≥F2) graft fibrosis. A 10-fold cross-validated LASSO model using clinical and radiomic features was developed. In total, 75 patients (29.5%) developed ≥F2 fibrosis by a median of 19 (4.3-121.8) months. The maximum liver attenuation at the venous phase (a radiomic feature reflecting venous perfusion), primary etiology, donor/recipient age, recurrence of disease, brain-dead donor, tacrolimus use at 3 months, and APRI score at 3 months were predictive of ≥F2 fibrosis. The combination of radiomics and the clinical features increased the AUC to 0.811 from 0.793 for the clinical-only model (p = 0.008) and from 0.664 for the radiomics-only model (p < 0.001) to predict future ≥F2 fibrosis. This pilot study exploring the role of radiomics demonstrates that the addition of radiomic features in a clinical model increased the model's performance. Further studies are required to investigate the generalizability of this experimental tool.

Predicting the recurrence risk of renal cell carcinoma after nephrectomy: potential role of CT-radiomics for adjuvant treatment decisions.

OBJECTIVES: Previous trial results suggest that only a small number of patients with non-metastatic renal cell carcinoma (RCC) benefit from adjuvant therapy. We assessed whether the addition of CT-based radiomics to established clinico-pathological biomarkers improves recurrence risk prediction for adjuvant treatment decisions. METHODS: This retrospective study included 453 patients with non-metastatic RCC undergoing nephrectomy. Cox models were trained to predict disease-free survival (DFS) using post-operative biomarkers (age, stage, tumor size and grade) with and without radiomics selected on pre-operative CT. Models were assessed using C-statistic, calibration, and decision curve analyses (repeated tenfold cross-validation). RESULTS: At multivariable analysis, one of four selected radiomic features (wavelet-HHL_glcm_ClusterShade) was prognostic for DFS with an adjusted hazard ratio (HR) of 0.44 (p = 0.02), along with American Joint Committee on Cancer (AJCC) stage group (III versus I, HR 2.90; p = 0.002), grade 4 (versus grade 1, HR 8.90; p = 0.001), age (per 10 years HR 1.29; p = 0.03), and tumor size (per cm HR 1.13; p = 0.003). The discriminatory ability of the combined clinical-radiomic model (C = 0.80) was superior to that of the clinical model (C = 0.78; p < 0.001). Decision curve analysis revealed a net benefit of the combined model when used for adjuvant treatment decisions. At an exemplary threshold probability of ≥ 25% for disease recurrence within 5 years, using the combined versus the clinical model was equivalent to treating 9 additional patients (per 1000 assessed) who would recur without treatment (i.e., true-positive predictions) with no increase in false-positive predictions. CONCLUSION: Adding CT-based radiomic features to established prognostic biomarkers improved post-operative recurrence risk assessment in our internal validation study and may help guide decisions regarding adjuvant therapy. KEY POINTS: In patients with non-metastatic renal cell carcinoma undergoing nephrectomy, CT-based radiomics combined with established clinical and pathological biomarkers improved recurrence risk assessment. Compared to a clinical base model, the combined risk model enabled superior clinical utility if used to guide decisions on adjuvant treatment.

Validating the total cancer location density metric for stratifying patients with low-risk localized prostate cancer at higher risk of grade group reclassification while on active surveillance.

PURPOSE: To validate a previously proposed prognostic metric, Total Cancer Location (TCLo) density, in a contemporary cohort of men with grade group (GG) 1 prostate cancer (PCa) on active surveillance (AS). METHODS: We evaluated 123 patients who entered AS with maximum GG1 PCa at diagnostic and/or confirmatory biopsy. TCLo was defined as the total number of PCa locations identified on both biopsy sessions. TCLo density was calculated as TCLo / prostate volume [ml]. Primary endpoint was progression-free survival (PFS), defined as time from confirmatory biopsy to grade group reclassification (GGR) on repeat biopsy or prostatectomy. Optimal cut-point for TCLo density was predefined in a previously reported cohort and applied to this contemporary cohort. Kaplan-Meier and multivariable Cox regression analysis were used to estimate the association of predictors with PFS. RESULTS: During median follow-up of 7.8 years, (IQR 7.3-8.2) 34 men had GGR. Using previously defined cut-points, PFS at 5-years was 60% (95% CI: 44%-81%) vs. 89% (95% CI: 83%-96%) in men with high (≥0.06 ml-1) vs. low (<0.06 ml-1) TCLo density, and 63% (95% CI: 48%-82%) vs. 90% (95% CI: 83%-96%) in men with high (≥3) vs. low (≤2) TCLo (log-rank test: P < 0.0001, respectively). Adjusting for age, prostate volume, percent of positive cores and PSA, both higher TCLo density (HR [per 0.01 ml-1 increase]: 1.18, 95% CI: 1.05-1.33, P = 0.005) and TCLo (HR: 1.69, 95% CI: 1.20-2.38, P = 0.002) were associated with shorter PFS. CONCLUSION: The previously suggested prognostic value of TCLo density was confirmed in this validation cohort. TCLo alone performed similarly well. Patients with high TCLo density (≥0.06 ml-1) or TCLo (>2) were at greater risk of GGR while on AS. With external validation, these metric may help guide risk-adapted surveillance protocols.

Correlation of transcriptional subtypes with a validated CT radiomics score in resectable pancreatic ductal adenocarcinoma.

OBJECTIVES: Transcriptional classifiers (Bailey, Moffitt and Collison) are key prognostic factors of pancreatic ductal adenocarcinoma (PDAC). Among these classifiers, the squamous, basal-like, and quasimesenchymal subtypes overlap and have inferior survival. Currently, only an invasive biopsy can determine these subtypes, possibly resulting in treatment delay. This study aimed to investigate the association between transcriptional subtypes and an externally validated preoperative CT-based radiomic prognostic score (Rad-score). METHODS: We retrospectively evaluated 122 patients who underwent resection for PDAC. All treatment decisions were determined at multidisciplinary tumor boards. Tumor Rad-score values from preoperative CT were dichotomized into high or llow categories. The primary endpoint was the correlation between the transcriptional subtypes and the Rad-score using multivariable linear regression, adjusting for clinical and histopathological variables (i.e., tumor size). Prediction of overall survival (OS) was secondary endpoint. RESULTS: The Bailey transcriptional classifier significantly associated with the Rad-score (coefficient = 0.31, 95% confidence interval [CI]: 0.13-0.44, p = 0.001). Squamous subtype was associated with high Rad-scores while non-squamous subtype was associated with low Rad-scores (adjusted p = 0.03). Squamous subtype and high Rad-score were both prognostic for OS at multivariable analysis with hazard ratios (HR) of 2.79 (95% CI: 1.12-6.92, p = 0.03) and 4.03 (95% CI: 1.42-11.39, p = 0.01), respectively. CONCLUSIONS: In patients with resectable PDAC, an externally validated prognostic radiomic model derived from preoperative CT is associated with the Bailey transcriptional classifier. Higher Rad-scores were correlated with the squamous subtype, while lower Rad-scores were associated with the less lethal subtypes (immunogenic, ADEX, pancreatic progenitor). KEY POINTS: • The transcriptional subtypes of PDAC have been shown to have prognostic importance but they require invasive biopsy to be assessed. • The Rad-score radiomic biomarker, which is obtained non-invasively from preoperative CT, correlates with the Bailey squamous transcriptional subtype and both are negative prognostic biomarkers. • The Rad-score is a promising non-invasive imaging biomarker for personalizing neoadjuvant approaches in patients undergoing resection for PDAC, although additional validation studies are required.

Iodine images in dual energy CT: A monocentric study benchmarking quantitative iodine concentration values of the healthy liver.

Dual energy computed tomography (DECT) allows the quantification of specific materials such as iodine contrast agent in human body tissue, potentially providing additional diagnostic data. Yet full diagnostic value can only be achieved if physiological normal values for iodine concentrations are known. We retrospectively evaluated abdominal DECT scans of 105 patients with healthy liver between March and August 2018 (age 17 to 86 years, 43 female and 62 male). The iodine concentrations within ROIs of the liver parenchyma as well as of the abdominal aorta and main portal vein were obtained. We evaluated the absolute iodine concentration and blood-normalized iodine concentrations relating the measured iodine concentration of the liver parenchyma to those of the supplying vessels. The influence of age and gender on the iodine uptake was assessed. The absolute iodine concentration was significantly different for the male and female cohort, but the difference was eliminated by the blood-normalized values. The average blood-normalized iodine concentrations were 2.107 mg/ml (+/- 0.322 mg/ml), 2.125 mg/ml (+/- 0.426 mg/ml) and 2.103 mg/ml (+/- 0.317 mg/ml) for the portal vein normalized, aorta normalized and mixed blood normalized iodine concentrations, respectively. A significant negative correlation between the patients' age and the iodine concentration was detected only for the blood-normalized values. A physiological range for iodine concentration in portal venous phase contrast enhanced DECT images can be defined for absolute and blood-normalized values. Deviations of blood-normalized iodine concentration values might be a robust biomarker for diagnostic evaluation. Patient age but not the gender influences the blood-normalized iodine concentrations in healthy liver parenchyma.

Iodine Images in Dual-energy CT: Detection of Hepatic Steatosis by Quantitative Iodine Concentration Values.

Hepatic steatosis is a common condition and an early manifestation of a systemic metabolic syndrome. As of today, there is no broadly accepted method for the diagnosis of hepatic steatosis in contrast-enhanced CT images. This retrospective study evaluates the potential of quantitative iodine values in portal venous phase iodine images in dual-energy CT (DECT) by measuring iodine concentrations in regions of interest (ROI) and analyzing the absolute iodine concentration of the liver parenchyma as well as three different blood-normalized iodine concentrations in a study cohort of 251 patients. An independent two sample t-test (p < 0.05) was used to compare the iodine concentrations of healthy and fatty liver. Diagnostic performance was assessed by ROC (receiver operating characteristic) curve analysis. The results showed significant differences between the average iodine concentration of healthy and fatty liver parenchyma for the absolute and for the blood-normalized iodine concentrations. The study concludes that the iodine uptake of the liver parenchyma is impaired by hepatic steatosis, and that the measurement of iodine concentration can provide a suitable method for the detection of hepatic steatosis in quantitative iodine images. Suitable thresholds of quantitative iodine concentration values for the diagnosis of hepatic steatosis are provided.

Exploring the value of using patient-oriented MRI reports in clinical practice - a pilot study.

PURPOSE: Standard radiology reports (SRR) are designed to communicate information between doctors. With many patients having instantaneous access to SRRs on patient portals, interpretation without guidance from doctors can cause anxiety and panic. In this pilot study, we designed a patient-centred prostate MRI template report (PACERR) to address some of these challenges and tested whether PACERRs improve patient knowledge and experience. MATERIALS AND METHODS: Patients booked for clinical prostate MRI were randomly assigned to SRR or SRR + PACERR. Questionnaires included multiple-choice that targeted 4 domains (understanding, usefulness, next steps, emotional experience) hypothesized to improve with patient-centred reports and short answer questions, testing knowledge regarding MRI results. Clinical encounters were observed and recorded to explore whether adding PACERR improved communication. Likert scaled-responses and short-answer questions were compared using Mann-Whitney U test and Kruskal-Wallis test. RESULTS: Of the 40 participants, the majority were MRI naïve (70%). Patients receiving a PACERR had higher scores in the categories of patient understanding (mean: 4.17 vs. 3.39, p=0.006), usefulness (mean: 4.58 vs. 3.07, p<0.001), and identifying next steps (mean: 1.89 vs. 3.03, p=0.003) but not emotional experience (mean: 4.18 vs. 3.79, p=0.22). PACERR participants found the layout and design more patient friendly (mean: 4.47 vs. 2.61, p<0.001) and easier to understand (mean: 4.37 vs. 2.38, p<0.001). In the knowledge section, overall, the PACERR arm scored better (87% vs. 56%, p=0.004). CONCLUSION: With the addition of prostate MRI PACERR, participants had better understanding of their results and felt more prepared to involve themselves in discussions with their doctor.

Prostate biopsy in the era of MRI-targeting: towards a judicious use of additional systematic biopsy.

OBJECTIVES: We aimed to develop and compare strategies that help optimize current prostate biopsy practice by identifying patients who may forgo concurrent systematic biopsy (SBx) in favor of MRI-targeted (TBx) alone. METHODS: Retrospective study on 745 patients who underwent combined MRI-TBx plus SBx. Primary outcome was the upgrade to clinically significant prostate cancer (csPCa; grade group ≥ 2) on SBx versus MRI-TBx. Variables (age, previous biopsy status, Prostate Imaging Reporting and Data System (PI-RADS) score, index lesion size/location, number of lesions, PSA, PSA density, prostate volume) associated with the primary outcome were identified by logistic regression and used for biopsy strategies. Clinical utility was assessed by decision curve analysis (DCA). RESULTS: SBx detected 47 (6%) additional men with csPCa. The risk of detecting csPCa uniquely on SBx was significantly lower in men with PI-RADS 5 (versus PI-RADS 3: OR 0.30, p = 0.03; versus PI-RADS 4: OR 0.33, p = 0.01), and previous negative biopsy (versus previous positive biopsy: OR 0.40, p = 0.007), and increased with age (per 10 years: OR 1.64, p = 0.016). No significant association was observed for other variables. DCA identified the following strategies as most useful: (a) avoid SBx in men with PI-RADS 5 and (b) additionally in those with previous negative biopsy, resulting in avoiding SBx in 201 (27%) and 429 (58%), while missing csPCa in 5 (1%) and 15 (2%) patients, respectively. CONCLUSION: Not all men benefit equally from the combination of SBx and MRI-TBx. SBx avoidance in men with PI-RADS 5 and/or previous negative biopsy may reduce the risk of excess biopsies with a low risk of missing csPCa. KEY POINTS: • In men undergoing MRI-targeted biopsy, the risk of detecting clinically significant prostate cancer (csPCa) only on additional systematic biopsy (SBx) decreased in men with PI-RADS 5, previous negative biopsy, and younger age. • Using these variables may help select men who could avoid the risk of excess SBx. • If missing csPCa in 5% was acceptable, forgoing SBx in men with PI-RADS 5 and/or previous negative biopsy enabled the highest net reduction in SBx.

Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation.

OBJECTIVES: In resectable pancreatic ductal adenocarcinoma (PDAC), few pre-operative prognostic biomarkers are available. Radiomics has demonstrated potential but lacks external validation. We aimed to develop and externally validate a pre-operative clinical-radiomic prognostic model. METHODS: Retrospective international, multi-center study in resectable PDAC. The training cohort included 352 patients (pre-operative CTs from five Canadian hospitals). Cox models incorporated (a) pre-operative clinical variables (clinical), (b) clinical plus CT-radiomics, and (c) post-operative TNM model, which served as the reference. Outcomes were overall (OS)/disease-free survival (DFS). Models were assessed in the validation cohort from Ireland (n = 215, CTs from 34 hospitals), using C-statistic, calibration, and decision curve analyses. RESULTS: The radiomic signature was predictive of OS/DFS in the validation cohort, with adjusted hazard ratios (HR) 2.87 (95% CI: 1.40-5.87, p < 0.001)/5.28 (95% CI 2.35-11.86, p < 0.001), respectively, along with age 1.02 (1.01-1.04, p = 0.01)/1.02 (1.00-1.04, p = 0.03). In the validation cohort, median OS was 22.9/37 months (p = 0.0092) and DFS 14.2/29.8 (p = 0.0023) for high-/low-risk groups and calibration was moderate (mean absolute errors 7%/13% for OS at 3/5 years). The clinical-radiomic model discrimination (C = 0.545, 95%: 0.543-0.546) was higher than the clinical model alone (C = 0.497, 95% CI 0.496-0.499, p < 0.001) or TNM (C = 0.525, 95% CI: 0.524-0.526, p < 0.001). Despite superior net benefit compared to the clinical model, the clinical-radiomic model was not clinically useful for most threshold probabilities. CONCLUSION: A multi-institutional pre-operative clinical-radiomic model for resectable PDAC prognostication demonstrated superior net benefit compared to a clinical model but limited clinical utility at external validation. This reflects inherent limitations of radiomics for PDAC prognostication, when deployed in real-world settings. KEY POINTS: • At external validation, a pre-operative clinical-radiomics prognostic model for pancreatic ductal adenocarcinoma (PDAC) outperformed pre-operative clinical variables alone or pathological TNM staging. • Discrimination and clinical utility of the clinical-radiomic model for treatment decisions remained low, likely due to heterogeneity of CT acquisition parameters. • Despite small improvements, prognosis in PDAC using state-of-the-art radiomics methodology remains challenging, mostly owing to its low discriminative ability. Future research should focus on standardization of CT protocols and acquisition parameters.

Prognostic value of early changes in CT-measured body composition in patients receiving chemotherapy for unresectable pancreatic cancer.

OBJECTIVES: Skeletal muscle mass is a prognostic factor in pancreatic ductal adenocarcinoma (PDAC). However, it remains unclear whether changes in body composition provide an incremental prognostic value to established risk factors, especially the Response Evaluation Criteria in Solid Tumors version 1.1 (RECISTv1.1). The aim of this study was to determine the prognostic value of CT-quantified body composition changes in patients with unresectable PDAC starting chemotherapy. METHODS: We retrospectively evaluated 105 patients with unresectable (locally advanced or metastatic) PDAC treated with FOLFIRINOX (n = 64) or gemcitabine-based (n = 41) first-line chemotherapy within a multicenter prospective trial. Changes (Δ) in skeletal muscle index (SMI), subcutaneous (SATI), and visceral adipose tissue index (VATI) between pre-chemotherapy and first follow-up CT were assessed. Cox regression models and covariate-adjusted survival curves were used to identify predictors of overall survival (OS). RESULTS: At multivariable analysis, adjusting for RECISTv1.1-response at first follow-up, ΔSMI was prognostic for OS with a hazard ratio (HR) of 1.2 (95% CI: 1.08-1.33, p = 0.001). No significant association with OS was observed for ΔSATI (HR: 1, 95% CI: 0.97-1.04, p = 0.88) and ΔVATI (HR: 1.01, 95% CI: 0.99-1.04, p = 0.33). At an optimal cutoff of 2.8 cm2/m2 per 30 days, the median survival of patients with high versus low ΔSMI was 143 versus 233 days (p < 0.001). CONCLUSIONS: Patients with a lower rate of skeletal muscle loss at first follow-up demonstrated improved survival for unresectable PDAC, regardless of their RECISTv1.1-category. Assessing ΔSMI at the first follow-up CT may be useful for prognostication, in addition to routine radiological assessment. KEY POINTS: • In patients with unresectable pancreatic ductal adenocarcinoma, change of skeletal muscle index (ΔSMI) in the early phase of chemotherapy is prognostic for overall survival, even after adjusting for Response Evaluation Criteria in Solid Tumors version 1.1 (RECISTv1.1) assessment at first follow-up. • Changes in adipose tissue compartments at first follow-up demonstrated no significant association with overall survival. • Integrating ΔSMI into routine radiological assessment may improve prognostic stratification and impact treatment decision-making at the first follow-up.

A Pre-TACE Radiomics Model to Predict HCC Progression and Recurrence in Liver Transplantation: A Pilot Study on a Novel Biomarker.

BACKGROUND: Despite transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC), a significant number of patients will develop progression on the liver transplant (LT) waiting list or disease recurrence post-LT. We sought to evaluate the feasibility of a pre-TACE radiomics model, an imaging-based tool to predict these adverse outcomes. METHODS: We analyzed the pre-TACE computed tomography images of patients waiting for a LT. The primary endpoint was a combined event that included waitlist dropout for tumor progression or tumor recurrence post-LT. The radiomic features were extracted from the largest HCC volume from the arterial and portal venous phase. A third set of features was created, combining the features from these 2 contrast phases. We applied a least absolute shrinkage and selection operator feature selection method and a support vector machine classifier. Three prognostic models were built using each feature set. The models' performance was compared using 5-fold cross-validated area under the receiver operating characteristic curves. RESULTS: . Eighty-eight patients were included, of whom 33 experienced the combined event (37.5%). The median time to dropout was 5.6 mo (interquartile range: 3.6-9.3), and the median time for post-LT recurrence was 19.2 mo (interquartile range: 6.1-34.0). Twenty-four patients (27.3%) dropped out and 64 (72.7%) patients were transplanted. Of these, 14 (21.9%) had recurrence post-LT. Model performance yielded a mean area under the receiver operating characteristic curves of 0.70 (±0.07), 0.87 (±0.06), and 0.81 (±0.06) for the arterial, venous, and the combined models, respectively. CONCLUSIONS: A pre-TACE radiomics model for HCC patients undergoing LT may be a useful tool for outcome prediction. Further external model validation with a larger sample size is required.

Avoiding Unnecessary Biopsy: MRI-based Risk Models versus a PI-RADS and PSA Density Strategy for Clinically Significant Prostate Cancer.

Background In validation studies, risk models for clinically significant prostate cancer (csPCa; Gleason score ≥3+4) combining multiparametric MRI and clinical factors have demonstrated poor calibration (over- and underprediction) and limited use in avoiding unnecessary prostate biopsies. Purpose MRI-based risk models following local recalibration were compared with a strategy that combined Prostate Imaging Data and Reporting System (PI-RADS; version 2) and prostate-specific antigen density (PSAd) to assess the potential reduction of unnecessary prostate biopsies. Materials and Methods This retrospective study included 385 patients without prostate cancer diagnosis who underwent multipara-metric MRI (PI-RADS category ≥3) and MRI-targeted biopsy between 2015 and 2019. Recalibration and selection of the best-performing MRI model (MRI-European Randomized Study of Screening for Prostate Cancer [ERSPC], van Leeuwen, Radtke, and Mehralivand models) were undertaken in cohort C1 (n = 242; 2015-2017). The impact on biopsy decisions was compared with an alternative strategy (no biopsy for PI-RADS category 3 plus PSAd < 0.1 ng/mL per milliliter) in cohort C2 (n = 143; 2018-2019). Discrimination, calibration, and clinical utility were assessed by using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. Results The prevalence of csPCa was 38% (93 of 242 patients) and 45% (64 of 143 patients) in cohorts C1 and C2, respectively. Decision curve analysis demonstrated the highest net benefit for the van Leeuwen and Mehralivand models in C1. Used for biopsy decisions in C2, van Leeuwen (AUC, 0.84; 95% CI: 0.77, 0.9) and Mehralivand (AUC, 0.79; 95% CI: 0.72, 0.86) enabled no net benefit at a risk threshold of 10%. Up to a risk threshold of 15%, net benefit remained inferior to the PI-RADS plus PSAd strategy, which avoided biopsy in 63 per 1000 men, without missing csPCa. Without prior recalibration in C1, three of four models (MRIERSPC, Radtke, Mehralivand) were poorly calibrated and not clinically useful in C2. Conclusion The number of unnecessary prostate biopsies in men with positive MRI may be safely reduced by using a prostate-specific antigen density-based strategy. In a risk-averse scenario, this strategy enabled better biopsy decisions compared with MRI-based risk models. ©RSNA, 2021 Online supplemental material is available for this article.

Improved differentiation between primary lung cancer and pulmonary metastasis by combining dual-energy CT-derived biomarkers with conventional CT attenuation.

OBJECTIVES: To assess the clinical utility of dual-energy CT (DE-CT)-derived iodine concentration (IC) and effective Z (Zeff) in addition to conventional CT attenuation (HU) for the discrimination between primary lung cancer (LC) and pulmonary metastases (PM) from different primary malignancies. METHODS: DE-CT scans of 79 patients with LC (3 histopathologic subgroups) and 89 patients with PM (5 histopathologic subgroups) were evaluated. Quantitative IC, Zeff, and conventional HU values were extracted and normalized to the thoracic aorta. Differences between groups were assessed by pairwise Welch's t test. Correlation and linear regression analyses were used to examine the relationship of imaging parameters in LC and PM. Diagnostic accuracy was measured by the area under receiver operator characteristic curve (AUC) and validated based on resampling methods. RESULTS: Significant differences between subgroups of LC and PMs were noted for all imaging parameters, with the highest number of significant pairs for IC. In univariate analysis, only IC was a significant diagnostic feature for discriminating LC from PM (p = 0.03). All quantitative imaging parameters correlated significantly (p < 0.0001, respectively), with the highest correlation between IC and Zeff (r = 0.91), followed by IC and HU (r = 0.76) and Zeff and HU (r = 0.73). Diagnostic models combining IC or Zeff with HU (IC+HU: AUC = 0.73; Zeff+HU: AUC = 0.69; IC+Zeff+HU: AUC = 0.73) were not significantly different and outperformed individual parameters (IC: AUC = 0.57; Zeff: AUC = 0.57; HU: AUC = 0.55) in diagnostic accuracy (p < 0.05, respectively). CONCLUSION: DE-CT-derived IC or Zeff and conventional HU represent complementary imaging parameters, which, if used in combination, may improve the differentiation between LC and PM. KEY POINTS: • Individual quantitative imaging parameters derived from DE-CT (iodine concentration, effective Z) and conventional CT (HU) provide complementary diagnostic information for the differentiation of primary lung cancer and pulmonary metastases. • A combination of conventional HU and DE-CT parameters enhances the diagnostic utility of individual parameters.

Validation of Prognostic Radiomic Features From Resectable Pancreatic Ductal Adenocarcinoma in Patients With Advanced Disease Undergoing Chemotherapy.

BACKGROUND: Radiomic features in pancreatic ductal adenocarcinoma (PDAC) often lack validation in independent test sets or are limited to early or late stage disease. Given the lethal nature of PDAC it is possible that there are similarities in radiomic features of both early and advanced disease reflective of aggressive biology. PURPOSE: To assess the performance of prognostic radiomic features previously published in patients with resectable PDAC in a test set of patients with unresectable PDAC undergoing chemotherapy. METHODS: The pre-treatment CT of 108 patients enrolled in a prospective chemotherapy trial were used as a test cohort for 2 previously published prognostic radiomic features in resectable PDAC (Sum Entropy and Cluster Tendency with square-root filter[Sqrt]). We assessed the performance of these 2 radiomic features for the prediction of overall survival (OS) and time to progression (TTP) using Cox proportional-hazard models. RESULTS: Sqrt Cluster Tendency was significantly associated with outcome with a hazard ratio (HR) of 1.27(for primary pancreatic tumor plus local nodes), (Confidence Interval(CI):1.01 -1.6, P-value = 0.039) for OS and a HR of 1.25(CI:1.00 -1.55, P-value = 0.047) for TTP. Sum entropy was not associated with outcomes. Sqrt Cluster Tendency remained significant in multivariate analysis. CONCLUSION: The CT radiomic feature Sqrt Cluster Tendency, previously demonstrated to be prognostic in resectable PDAC, remained a significant prognostic factor for OS and TTP in a test set of unresectable PDAC patients. This radiomic feature warrants further investigation to understand its biologic correlates and CT applicability in PDAC patients.

Dual-energy CT parameters in correlation to MRI-based apparent diffusion coefficient: evaluation in rectal cancer after radiochemotherapy.

BACKGROUND: Rectal cancer (RC) is a frequent malignancy for which magnetic resonance imaging (MRI) is the most common and accurate imaging. Iodine concentration (IC) can be quantified with spectral dual-layer computed tomography CT (DL-CT), which could improve imaging of RC, especially for evaluation of response to radiochemotherapy (RCT). PURPOSE: To compare a DL-CT system to MRI as the non-invasive imaging gold standard for imaging of RC to evaluate the possibility of a response evaluation with DL-CT. MATERIAL AND METHODS: Eleven patients who received DL-CT as well as MRI before and after RCT of RC were retrospectively included into this study. For each examination, a region of interest (ROI) was placed within the tumor. For MRI, the mean apparent diffusion coefficient (ADC) was assessed. For DL-CT, IC, z-effective, and Hounsfield Units (HU) were measured. IC, z-effective, and HU were normalized to the aorta. ADC was correlated to absolute and relative normalized IC, z-effective, and HU with Spearman's ρ. Differences before and after treatment were tested with Wilcoxon signed-rank test. RESULTS: HU, IC, and Z-effective values in DL-CT images decreased significantly after RCT (P<0.01 for each comparison). The mean ADC increased significantly after RCT. Spearman's ρ of the absolute IC difference and the absolute ADC (both before and after RCT) is high and significant (ρ = 0.73; P = 0.01), whereas the ρ-value for z-effective (ρ = 0.56) or HU (ρ = 0.45) to ADC was lower and non-significant. CONCLUSION: Response evaluation of RC after RCT could be possible with DL-CT via the measurement of IC.

Does the Visibility of Grade Group 1 Prostate Cancer on Baseline Multiparametric Magnetic Resonance Imaging Impact Clinical Outcomes?

PURPOSE: We assessed whether the visibility of Grade Group (GG) 1 prostate cancer on baseline multiparametric magnetic resonance imaging affects clinical outcomes. MATERIALS AND METHODS: We evaluated 454 men who underwent multiparametric magnetic resonance imaging between 2006 and 2018 with maximum GG1 prostate cancer inclusive of magnetic resonance imaging targeted biopsy. Multiparametric magnetic resonance imaging was graded as negative, equivocal or positive. Assessed outcomes were treatment-free survival, biopsy upgrade-free survival and unfavorable disease at radical prostatectomy (pT 3 or greater and/or GG3 or greater). Kaplan-Meier and multivariable Cox proportional hazard analyses were used to estimate the impact of multiparametric magnetic resonance imaging and clinicopathological variables (age, year, prostate specific antigen density and measures of tumor volume on biopsy) on outcomes. RESULTS: During followup (median 45.2 months) 61 men had disease upgraded on followup biopsy and 139 underwent definitive treatment. In men with negative, equivocal and positive baseline multiparametric magnetic resonance imaging at 5 years, treatment-free survival was 79%, 73% and 49% (p <0.0001), treatment-free survival was 89%, 82% and 70% (p=0.002), and survival without unfavorable disease at radical prostatectomy was 98%, 98% and 86% (p=0.007), respectively. At multivariable analysis positive (HR 1.93, 95% CI 1.21-3.09, p=0.006) and equivocal multiparametric magnetic resonance imaging (HR 2.02, 95% CI 1.11-3.68, p=0.02) were associated with shorter treatment-free survival, and positive multiparametric magnetic resonance imaging was a significant prognostic factor for upgrade-free survival (HR 2.03, 95% CI 1.06-3.86, p=0.03) and unfavorable disease at radical prostatectomy (HR 4.45, 95% CI 1.39-18.17, p=0.01). CONCLUSIONS: Men with positive multiparametric magnetic resonance imaging and GG1 prostate cancer on magnetic resonance imaging targeted biopsy are at increased risk for intervention, upgrading and unfavorable disease at radical prostatectomy compared to those with multiparametric magnetic resonance imaging invisible GG1 prostate cancer.

Using decision curve analysis to benchmark performance of a magnetic resonance imaging-based deep learning model for prostate cancer risk assessment.

OBJECTIVES: To benchmark the performance of a calibrated 3D convolutional neural network (CNN) applied to multiparametric MRI (mpMRI) for risk assessment of clinically significant prostate cancer (csPCa) using decision curve analysis (DCA). METHODS: We retrospectively analyzed 499 patients who had positive mpMRI (PI-RADSv2 ≥ 3) and MRI-targeted biopsy. The training cohort comprised 449 men, including a calibration set of 50 men. Biopsy decision strategies included using risk estimates from the CNN (original and calibrated), to perform biopsy in men with PI-RADSv2 ≥ 4 only, or additionally in men with PI-RADSv2 3 and PSA density (PSAd) ≥ 0.15 ng/ml/ml. Discrimination, calibration and clinical usefulness in the unseen test cohort (n = 50) were assessed using C-statistic, calibration plots and DCA, respectively. RESULTS: The calibrated CNN achieved moderate calibration (Hosmer-Lemeshow calibration test, p = 0.41) and good discrimination (C = 0.85). DCA revealed consistently higher net benefit and net reduction in biopsies for the calibrated CNN compared with the original CNN, PI-RADSv2 ≥ 4 and the combined strategy of PI-RADSv2 and PSAd. Original CNN predictions were severely miscalibrated (p < 0.0001) resulting in net harm compared with a 'biopsy all' patients strategy. At-risk thresholds ≥ 10% using the calibrated CNN and the combined strategy reduced the number of biopsies by an estimated 201 and 55 men, respectively, per 1000 men at risk, without missing csPCa, while original CNN and PI-RADSv2 ≥ 4 could not achieve a net reduction in biopsies. CONCLUSIONS: DCA revealed that our calibrated 3D-CNN resulted in fewer unnecessary biopsies compared with using PI-RADSv2 alone or in combination with PSAd. CNN calibration is important in achieving clinical utility. KEY POINTS: • A 3D deep learning model applied to multiparametric MRI may help to prevent unnecessary prostate biopsies in patients eligible for MRI-targeted biopsy. • Owing to miscalibration, original risk estimates by the deep learning model require prior calibration to enable clinical utility. • Decision curve analysis confirmed a net benefit of using our calibrated deep learning model for biopsy decisions compared with alternative strategies, including PI-RADSv2 alone and in combination with prostate-specific antigen density.