Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies.

OBJECTIVES: To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS). METHODS: This retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012-10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics. RESULTS: A total of 1604 patients aged 67 (interquartile range, 61-73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80-0.97) and 0.84 (0.70-0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68-0.74)/0.73 (0.70-0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10-20%) to 43% (30-44%; p < 0.001) with similar sensitivity (93% (89-96%)/97% (94-99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104-142)/165 (146-185); p < 0.001). CONCLUSION: Using sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers. CLINICAL RELEVANCE STATEMENT: Transition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers. KEY POINTS: • Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer. • PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only. • TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.

Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI.

Background Clinically significant prostate cancer (PCa) diagnosis at MRI requires accurate and efficient radiologic interpretation. Although artificial intelligence may assist in this task, lack of transparency has limited clinical translation. Purpose To develop an explainable artificial intelligence (XAI) model for clinically significant PCa diagnosis at biparametric MRI using Prostate Imaging Reporting and Data System (PI-RADS) features for classification justification. Materials and Methods This retrospective study included consecutive patients with histopathologic analysis-proven prostatic lesions who underwent biparametric MRI and biopsy between January 2012 and December 2017. After image annotation by two radiologists, a deep learning model was trained to detect the index lesion; classify PCa, clinically significant PCa (Gleason score ≥ 7), and benign lesions (eg, prostatitis); and justify classifications using PI-RADS features. Lesion- and patient-based performance were assessed using fivefold cross validation and areas under the receiver operating characteristic curve. Clinical feasibility was tested in a multireader study and by using the external PROSTATEx data set. Statistical evaluation of the multireader study included Mann-Whitney U and exact Fisher-Yates test. Results Overall, 1224 men (median age, 67 years; IQR, 62-73 years) had 3260 prostatic lesions (372 lesions with Gleason score of 6; 743 lesions with Gleason score of ≥ 7; 2145 benign lesions). XAI reliably detected clinically significant PCa in internal (area under the receiver operating characteristic curve, 0.89) and external test sets (area under the receiver operating characteristic curve, 0.87) with a sensitivity of 93% (95% CI: 87, 98) and an average of one false-positive finding per patient. Accuracy of the visual and textual explanations of XAI classifications was 80% (1080 of 1352), confirmed by experts. XAI-assisted readings improved the confidence (4.1 vs 3.4 on a five-point Likert scale; P = .007) of nonexperts in assessing PI-RADS 3 lesions, reducing reading time by 58 seconds (P = .009). Conclusion The explainable AI model reliably detected and classified clinically significant prostate cancer and improved the confidence and reading time of nonexperts while providing visual and textual explanations using well-established imaging features. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chapiro in this issue.

Quantitative dual-energy CT as a nondestructive tool to identify indicators for fossilized bone in vertebrate paleontology.

Dual-energy computed tomography (DECT) is an imaging technique that combines nondestructive morphological cross-sectional imaging of objects and the quantification of their chemical composition. However, its potential to assist investigations in paleontology has not yet been explored. This study investigates quantitative DECT for the nondestructive density- and element-based material decomposition of fossilized bones. Specifically, DECT was developed and validated for imaging-based calcium and fluorine quantification in bones of five fossil vertebrates from different geological time periods and of one extant vertebrate. The analysis shows that DECT material maps can differentiate bone from surrounding sediment and reveals fluorine as an imaging marker for fossilized bone and a reliable indicator of the age of terrestrial fossils. Moreover, the jaw bone mass of Tyrannosaurus rex showed areas of particularly high fluorine concentrations on DECT, while conventional CT imaging features supported the diagnosis of chronic osteomyelitis. These findings highlight the relevance of radiological imaging techniques in the natural sciences by introducing quantitative DECT imaging as a nondestructive approach for material decomposition in fossilized objects, thereby potentially adding to the toolbox of paleontological studies.

Response assessment methods for patients with hepatic metastasis from rare tumor primaries undergoing transarterial chemoembolization.

PURPOSE: This study assessed the response to conventional transarterial chemoembolization (cTACE) in patients with liver metastases from rare tumor primaries using one-dimensional (1D) and three-dimensional (3D) quantitative response assessment methods, and investigate the relationship of lipiodol deposition in predicting response. MATERIALS AND METHODS: This retrospective bicentric study included 16 patients with hepatic metastases from rare tumors treated with cTACE between 2002 and 2017. Multi-phasic MR imaging obtained before and after cTACE was used for assessment of response. Response evaluation criteria in solid tumors (RECIST) and modified-RECIST (mRECIST) were utilized for 1D response assessment, and volumetric RECIST (vRECIST) and enhancement-based quantitative European Association for Study of the Liver EASL (qEASL) were used for 3D response assessment. The same day post-cTACE CT scan was analyzed to quantify intratumoral lipiodol deposition (%). RESULTS: The mean and standard deviation (SD) of diameter of treated lesions per targeted area was 7.5 ± 5.4 cm, and the mean and SD of number of metastases in each targeted area was 4.2 ± 4.6. cTACE was technically successful in all patients, without major complications. While RECIST and vRECIST methods did not allocate patients with partial response, mRECIST and qEASL identified patients with partial response. Intratumoral lipiodol deposition significantly predicted treatment response according qEASL (R2 = 0.470, p < 0.01), while no association was shown between lipiodol deposition within treated tumor area and RECIST or mRECIST (p > 0.212). CONCLUSION: 3D quantitative volumetric response analysis can be used for stratification of response to cTACE in patients with hepatic metastases originating from rare primary tumors. Lipiodol deposition could potentially be used as an early surrogate to predict response to cTACE.

Impact of Chemoembolic Regimen on Immune Cell Recruitment and Immune Checkpoint Marker Expression following Transcatheter Arterial Chemoembolization in a VX2 Rabbit Liver Tumor Model.

PURPOSE: To characterize the effects of commonly used transcatheter arterial chemoembolization (TACE) regimens on the immune response and immune checkpoint marker expression using a VX2 rabbit liver tumor model. MATERIALS AND METHODS: Twenty-four VX2 liver tumor-bearing New Zealand white rabbits were assigned to 7 groups (n = 3 per group) undergoing locoregional therapy as follows: (a) bicarbonate infusion without embolization, (b) conventional TACE (cTACE) using a water-in-oil emulsion containing doxorubicin mixed 1:2 with Lipiodol, drug-eluting embolic-TACE with either (c) idarubicin-eluting Oncozene microspheres (40 µm) or (d) doxorubicin-eluting Lumi beads (40-90 µm). For each therapy arm (b-d), a tandem set of 3 animals with additional bicarbonate infusion before TACE was added, to evaluate the effect of pH modification on the immune response. Three untreated rabbits served as controls. Tissue was harvested 24 hours after treatment, followed by digital immunohistochemistry quantification (counts/µm2 ± SEM) of tumor-infiltrating cluster of differentiation 3+ T-lymphocytes, human leukocyte antigen DR type antigen-presenting cells (APCs), cytotoxic T-lymphocyte-associated protein-4 (CTLA-4), and programmed cell death protein-1 (PD-1)/PD-1 ligand (PD-L1) pathway axis expression. RESULTS: Lumi-bead TACE induced significantly more intratumoral T-cell and APC infiltration than cTACE and Oncozene-microsphere TACE. Additionally, tumors treated with Lumi-bead TACE expressed significantly higher intratumoral immune checkpoint markers compared with cTACE and Oncozene-microsphere TACE. Neoadjuvant bicarbonate demonstrated the most pronounced effect on cTACE and resulted in a significant increase in intratumoral cluster of differentiation 3+ T-cell infiltration compared with cTACE alone. CONCLUSIONS: This preclinical study revealed significant differences in evoked tumor immunogenicity depending on the choice of chemoembolic regimen for TACE.

Comparison of metabolic and immunologic responses to transarterial chemoembolization with different chemoembolic regimens in a rabbit VX2 liver tumor model.

OBJECTIVES: The goal of this study was to investigate the effects of TACE using Lipiodol, Oncozene™ drug-eluting embolics (DEEs), or LUMI™-DEEs alone, or combined with bicarbonate on the metabolic and immunological tumor microenvironment in a rabbit VX2 tumor model. METHODS: VX2 liver tumor-bearing rabbits were assigned to five groups. MRI and extracellular pH (pHe) mapping using Biosensor Imaging of Redundant Deviation in Shifts (BIRDS) were performed before and after intra-arterial therapy with conventional TACE (cTACE), DEE-TACE with Idarubicin-eluting Oncozene™-DEEs, or Doxorubicin-eluting LUMI™-DEEs, each with or without prior bicarbonate infusion, and in untreated rabbits or treated with intra-arterial bicarbonate only. Imaging results were validated with immunohistochemistry (IHC) staining of cell viability (PCNA, TUNEL) and immune response (HLA-DR, CD3). Statistical analysis was performed using Mann-Whitney U test. RESULTS: pHe mapping revealed that combining cTACE with prior bicarbonate infusion significantly increased tumor pHe compared to control (p = 0.0175) and cTACE alone (p = 0.0025). IHC staining revealed peritumoral accumulation of HLA-DR+ antigen-presenting cells and CD3 + T-lymphocytes in controls. cTACE-treated tumors showed reduced immune infiltration, which was restored through combination with bicarbonate. DEE-TACE with Oncozene™-DEEs induced moderate intratumoral and marked peritumoral infiltration, which was slightly reduced with bicarbonate. Addition of bicarbonate prior to LUMI™-beads enhanced peritumoral immune cell infiltration compared to LUMI™-beads alone and resulted in the strongest intratumoral immune cell infiltration across all treated groups. CONCLUSIONS: The choice of chemoembolic regimen for TACE strongly affects post-treatment TME pHe and the ability of immune cells to accumulate and infiltrate the tumor tissue. KEY POINTS: • Combining conventional transarterial chemotherapy with prior bicarbonate infusion increases the pHe towards a more physiological value (p = 0.0025). • Peritumoral infiltration and intratumoral accumulation patterns of antigen-presenting cells and T-lymphocytes after transarterial chemotherapy were dependent on the choice of the chemoembolic regimen. • Combination of intra-arterial treatment with Doxorubicin-eluting LUMI™-beads and bicarbonate infusion resulted in the strongest intratumoral presence of immune cells (positivity index of 0.47 for HLADR+-cells and 0.62 for CD3+-cells).

Deep learning-assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver.

OBJECTIVES: To train a deep learning model to differentiate between pathologically proven hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical imaging features on MRI. METHODS: This IRB-approved retrospective study included 118 patients with 150 lesions (93 (62%) HCC and 57 (38%) non-HCC) pathologically confirmed through biopsies (n = 72), resections (n = 29), liver transplants (n = 46), and autopsies (n = 3). Forty-seven percent of HCC lesions showed atypical imaging features (not meeting Liver Imaging Reporting and Data System [LI-RADS] criteria for definitive HCC/LR5). A 3D convolutional neural network (CNN) was trained on 140 lesions and tested for its ability to classify the 10 remaining lesions (5 HCC/5 non-HCC). Performance of the model was averaged over 150 runs with random sub-sampling to provide class-balanced test sets. A lesion grading system was developed to demonstrate the similarity between atypical HCC and non-HCC lesions prone to misclassification by the CNN. RESULTS: The CNN demonstrated an overall accuracy of 87.3%. Sensitivities/specificities for HCC and non-HCC lesions were 92.7%/82.0% and 82.0%/92.7%, respectively. The area under the receiver operating curve was 0.912. CNN's performance was correlated with the lesion grading system, becoming less accurate the more atypical imaging features the lesions showed. CONCLUSION: This study provides proof-of-concept for CNN-based classification of both typical- and atypical-appearing HCC lesions on multi-phasic MRI, utilizing pathologically confirmed lesions as "ground truth." KEY POINTS: • A CNN trained on atypical appearing pathologically proven HCC lesions not meeting LI-RADS criteria for definitive HCC (LR5) can correctly differentiate HCC lesions from other liver malignancies, potentially expanding the role of image-based diagnosis in primary liver cancer with atypical features. • The trained CNN demonstrated an overall accuracy of 87.3% and a computational time of < 3 ms which paves the way for clinical application as a decision support instrument.

Prospective study of Lipiodol distribution as an imaging marker for doxorubicin pharmacokinetics during conventional transarterial chemoembolization of liver malignancies.

OBJECTIVES: To evaluate the prognostic potential of Lipiodol distribution for the pharmacokinetic (PK) profiles of doxorubicin (DOX) and doxorubicinol (DOXOL) after conventional transarterial chemoembolization (cTACE). METHODS: This prospective clinical trial ( ClinicalTrials.gov : NCT02753881) included 30 consecutive participants with liver malignancies treated with cTACE (5/2016-10/2018) using 50 mg DOX/10 mg mitomycin C emulsified 1:2 with ethiodized oil (Lipiodol). Peripheral blood was sampled at 10 timepoints for standard non-compartmental analysis of peak concentrations (Cmax) and area under the curve (AUC) with dose normalization (DN). Imaging markers included Lipiodol distribution on post-cTACE CT for patient stratification into 1 segment (n = 10), ≥ 2 segments (n = 10), and lobar cTACE (n = 10), and baseline enhancing tumor volume (ETV). Adverse events (AEs) and tumor response on MRI were recorded 3-4 weeks post-cTACE. Statistics included repeated measurement ANOVA (RM-ANOVA), Mann-Whitney, Kruskal-Wallis, Fisher's exact test, and Pearson correlation. RESULTS: Hepatocellular (n = 26), cholangiocarcinoma (n = 1), and neuroendocrine metastases (n = 3) were included. Stratified according to Lipiodol distribution, DOX-Cmax increased from 1 segment (DOX-Cmax, 83.94 ± 75.09 ng/mL; DN-DOX-Cmax, 2.67 ± 2.02 ng/mL/mg) to ≥ 2 segments (DOX-Cmax, 139.66 ± 117.73 ng/mL; DN-DOX-Cmax, 3.68 ± 4.20 ng/mL/mg) to lobar distribution (DOX-Cmax, 334.35 ± 215.18 ng/mL; DN-DOX-Cmax, 7.11 ± 4.24 ng/mL/mg; p = 0.036). While differences in DN-DOX-AUC remained insignificant, RM-ANOVA revealed significant separation of time concentration curves for DOX (p = 0.023) and DOXOL (p = 0.041) comparing 1, ≥ 2 segments, and lobar cTACE. Additional indicators of higher DN-DOX-Cmax were high ETV (p = 0.047) and Child-Pugh B (p = 0.009). High ETV and tumoral Lipiodol coverage also correlated with tumor response. AE occurred less frequently after segmental cTACE. CONCLUSIONS: This prospective clinical trial provides updated PK data revealing Lipiodol distribution as an imaging marker predictive of DOX-Cmax and tumor response after cTACE in liver cancer. KEY POINTS: • Prospective pharmacokinetic analysis after conventional TACE revealed Lipiodol distribution (1 vs. ≥ 2 segments vs. lobar) as an imaging marker predictive of doxorubicin peak concentrations (Cmax). • Child-Pugh B class and tumor hypervascularization, measurable as enhancing tumor volume (ETV) at baseline, were identified as additional predictors for higher dose-normalized doxorubicin Cmax after conventional TACE. • ETV at baseline and tumoral Lipiodol coverage can serve as predictors of volumetric tumor response after conventional TACE according to quantitative European Association for the Study of the Liver (qEASL) criteria.

Idarubicin-Loaded ONCOZENE Drug-Eluting Bead Chemoembolization in a Rabbit Liver Tumor Model: Investigating Safety, Therapeutic Efficacy, and Effects on Tumor Microenvironment.

PURPOSE: To investigate toxicity, efficacy, and microenvironmental effects of idarubicin-loaded 40-µm and 100-µm drug-eluting embolic (DEE) transarterial chemoembolization in a rabbit liver tumor model. MATERIALS AND METHODS: Twelve male New Zealand White rabbits with orthotopically implanted VX2 liver tumors were assigned to DEE chemoembolization with 40-µm (n = 5) or 100-µm (n = 4) ONCOZENE microspheres or no treatment (control; n = 3). At 24-72 hours postprocedurally, multiparametric magnetic resonance (MR) imaging including dynamic contrast-enhanced (DCE), diffusion-weighted imaging (DWI), and biosensor imaging of redundant deviation in shifts (BIRDS) was performed to assess extracellular pH (pHe), followed by immediate euthanasia. Laboratory parameters and histopathologic ex vivo analysis included fluorescence confocal microscopy and immunohistochemistry. RESULTS: DCE MR imaging demonstrated a similar degree of devascularization of embolized tumors for both microsphere sizes (mean arterial enhancement, 8% ± 12 vs 36% ± 51 in controls; P = .07). Similarly, DWI showed postprocedural increases in diffusion across the entire lesion (apparent diffusion coefficient, 1.89 × 10-3 mm2/s ± 0.18 vs 2.34 × 10-3 mm2/s ± 0.18 in liver; P = .002). BIRDS demonstrated profound tumor acidosis at baseline (mean pHe, 6.79 ± 0.08 in tumor vs 7.13 ± 0.08 in liver; P = .02) and after chemoembolization (6.8 ± 0.06 in tumor vs 7.1 ± 0.04 in liver; P = .007). Laboratory and ex vivo analyses showed central tumor core penetration and greater increase in liver enzymes for 40-µm vs 100-µm microspheres. Inhibition of cell proliferation, intratumoral hypoxia, and limited idarubicin elution were equally observed with both sphere sizes. CONCLUSIONS: Noninvasive multiparametric MR imaging visualized chemoembolic effects in tumor and tumor microenvironment following DEE chemoembolization. Devascularization, increased hypoxia, coagulative necrosis, tumor acidosis, and limited idarubicin elution suggest ischemia as the predominant therapeutic mechanism. Substantial size-dependent differences indicate greater toxicity with the smaller microsphere diameter.

Primary Neuroendocrine Neoplasms of the Breast: Case Series and Literature Review.

Primary neuroendocrine carcinoma of the breast (NECB) as defined by the World Health Organization (WHO) in 2012 is a rare, but possibly under-diagnosed entity. It is heterogeneous as it entails a wide spectrum of diseases comprising both well-differentiated neuroendocrine tumors of the breast as well as highly aggressive small cell carcinomas. Retrospective screening of hospital charts of 612 patients (2008-2019) from our specialized outpatient unit for neuroendocrine neoplasia revealed five patients diagnosed with NECB. Given the low prevalence of these malignancies, correct diagnosis remains a challenge that requires an interdisciplinary approach. Specifically, NECB may be misclassified as carcinoma of the breast with neuroendocrine differentiation, carcinomas of the breast of no special type/invasive ductal carcinoma, or a metastasis to the breast. Therefore, this study presents multifaceted characteristics as well as the clinical course of these patients and discusses the five cases from our institution in the context of available literature.

Quantification of contrast-uptake as imaging biomarker for disease progression of renal cell carcinoma after tumor ablation.

BACKGROUND: The prognosis of patients with renal cell carcinoma (RCC) depends greatly on the presence of extra-renal metastases. PURPOSE: To investigate the value of total tumor volume (TTV) and enhancing tumor volume (ETV) as three-dimensional (3D) quantitative imaging biomarkers for disease aggressiveness in patients with RCC. MATERIAL AND METHODS: Retrospective, HIPAA-compliant, IRB-approved study including 37 patients with RCC treated with image-guided thermal ablation during 2007-2015. TNM stage, RENAL Nephrometry Score, largest tumor diameter, TTV, and ETV were assessed on cross-sectional imaging at baseline and correlated with outcome measurements. The primary outcome was time-to-occurrence of extra-renal metastases and the secondary outcome was progression-free survival (PFS). Correlation was assessed using a Cox regression model and differences in outcomes were shown by Kaplan-Meier plots with significance and odds ratios (OR) calculated by Log-rank test/generalized Wilcoxon and continuity-corrected Woolf logit method. RESULTS: Patients with a TTV or ETV > 5 cm3 were more likely to develop distant metastases compared to patients with TTV (OR 6.69, 95% confidence interval [CI] 0.33-134.4, P=0.022) or ETV (OR 8.48, 95% CI 0.42-170.1, P=0.016) < 5 cm3. Additionally, PFS was significantly worse in patients with larger ETV (P = 0.039; median PFS 51.87 months vs. 69.97 months). In contrast, stratification by median value of the established, caliper-based measurements showed no significant correlation with outcome parameters. CONCLUSION: ETV, as surrogate of lesion vascularity, is a sensitive imaging biomarker for occurrence of extra-renal metastatic disease and PFS in patients with RCC.

Extracellular pH mapping of liver cancer on a clinical 3T MRI scanner.

PURPOSE: To demonstrate feasibility of developing a noninvasive extracellular pH (pHe ) mapping method on a clinical MRI scanner for molecular imaging of liver cancer. METHODS: In vivo pHe mapping has been demonstrated on preclinical scanners (e.g., 9.4T, 11.7T) with Biosensor Imaging of Redundant Deviation in Shifts (BIRDS), where the pHe readout by 3D chemical shift imaging (CSI) depends on hyperfine shifts emanating from paramagnetic macrocyclic chelates like TmDOTP5- which upon extravasation from blood resides in the extracellular space. We implemented BIRDS-based pHe mapping on a clinical 3T Siemens scanner, where typically diamagnetic 1 H signals are detected using millisecond-long radiofrequency (RF) pulses, and 1 H shifts span over ±10 ppm with long transverse (T2 , 102 ms) and longitudinal (T1 , 103 ms) relaxation times. We modified this 3D-CSI method for ultra-fast acquisition with microsecond-long RF pulses, because even at 3T the paramagnetic 1 H shifts of TmDOTP5- have millisecond-long T2 and T1 and ultra-wide chemical shifts (±200 ppm) as previously observed in ultra-high magnetic fields. RESULTS: We validated BIRDS-based pH in vitro with a pH electrode. We measured pHe in a rabbit model for liver cancer using VX2 tumors, which are highly vascularized and hyperglycolytic. Compared to intratumoral pHe (6.8 ± 0.1; P < 10-9 ) and tumor's edge pHe (6.9 ± 0.1; P < 10-7 ), liver parenchyma pHe was significantly higher (7.2 ± 0.1). Tumor localization was confirmed with histopathological markers of necrosis (hematoxylin and eosin), glucose uptake (glucose transporter 1), and tissue acidosis (lysosome-associated membrane protein 2). CONCLUSION: This work demonstrates feasibility and potential clinical translatability of high-resolution pHe mapping to monitor tumor aggressiveness and therapeutic outcome, all to improve personalized cancer treatment planning.

Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features.

OBJECTIVES: To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier. METHODS: A convolutional neural network (CNN) was engineered and trained to classify six hepatic tumor entities using 494 lesions on multi-phasic MRI, described in Part 1. A subset of each lesion class was labeled with up to four key imaging features per lesion. A post hoc algorithm inferred the presence of these features in a test set of 60 lesions by analyzing activation patterns of the pre-trained CNN model. Feature maps were generated that highlight regions in the original image that correspond to particular features. Additionally, relevance scores were assigned to each identified feature, denoting the relative contribution of a feature to the predicted lesion classification. RESULTS: The interpretable deep learning system achieved 76.5% positive predictive value and 82.9% sensitivity in identifying the correct radiological features present in each test lesion. The model misclassified 12% of lesions. Incorrect features were found more often in misclassified lesions than correctly identified lesions (60.4% vs. 85.6%). Feature maps were consistent with original image voxels contributing to each imaging feature. Feature relevance scores tended to reflect the most prominent imaging criteria for each class. CONCLUSIONS: This interpretable deep learning system demonstrates proof of principle for illuminating portions of a pre-trained deep neural network's decision-making, by analyzing inner layers and automatically describing features contributing to predictions. KEY POINTS: • An interpretable deep learning system prototype can explain aspects of its decision-making by identifying relevant imaging features and showing where these features are found on an image, facilitating clinical translation. • By providing feedback on the importance of various radiological features in performing differential diagnosis, interpretable deep learning systems have the potential to interface with standardized reporting systems such as LI-RADS, validating ancillary features and improving clinical practicality. • An interpretable deep learning system could potentially add quantitative data to radiologic reports and serve radiologists with evidence-based decision support.

Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.

OBJECTIVES: To develop and validate a proof-of-concept convolutional neural network (CNN)-based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI. METHODS: A custom CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (n = 434) and test (n = 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engineering was finalized, classification accuracy for the final CNN was compared with two board-certified radiologists on an identical unseen test set. RESULTS: The DLS demonstrated a 92% accuracy, a 92% sensitivity (Sn), and a 98% specificity (Sp). Test set performance in a single run of random unseen cases showed an average 90% Sn and 98% Sp. The average Sn/Sp on these same cases for radiologists was 82.5%/96.5%. Results showed a 90% Sn for classifying hepatocellular carcinoma (HCC) compared to 60%/70% for radiologists. For HCC classification, the true positive and false positive rates were 93.5% and 1.6%, respectively, with a receiver operating characteristic area under the curve of 0.992. Computation time per lesion was 5.6 ms. CONCLUSION: This preliminary deep learning study demonstrated feasibility for classifying lesions with typical imaging features from six common hepatic lesion types, motivating future studies with larger multi-institutional datasets and more complex imaging appearances. KEY POINTS: • Deep learning demonstrates high performance in the classification of liver lesions on volumetric multi-phasic MRI, showing potential as an eventual decision-support tool for radiologists. • Demonstrating a classification runtime of a few milliseconds per lesion, a deep learning system could be incorporated into the clinical workflow in a time-efficient manner.

Quantitative Imaging Biomarkers for 90Y Distribution on Bremsstrahlung SPECT After Resin-Based Radioembolization.

Our purpose was to identify baseline imaging features in patients with liver cancer that correlate with 90Y distribution on postprocedural SPECT and predict tumor response to transarterial radioembolization (TARE). Methods: This retrospective study was approved by the institutional review board and included 38 patients with hepatocellular carcinoma (HCC) (n = 23; 18/23 men; mean age, 62.39 ± 8.62 y; 34 dominant tumors) and non-HCC hepatic malignancies (n = 15; 9/15 men; mean age, 61.13 ± 11.51 y; 24 dominant tumors) who underwent 40 resin-based TARE treatments (August 2012 to January 2018). Multiphasic contrast-enhanced MRI or CT was obtained before and Bremsstrahlung SPECT within 2 h after TARE. Total tumor volume (cm3) and enhancing tumor volume (ETV [cm3] and % of total tumor volume), and total and enhancing tumor burden (%), were volumetrically assessed on baseline imaging. Up to 2 dominant tumors per treated lobe were analyzed. After multimodal image registration of baseline imaging and SPECT/CT, 90Y distribution was quantified on SPECT as tumor-to-normal-liver ratio (TNR). Response was assessed according to RECIST1.1 and quantitative European Association for the Study of the Liver criteria. Clinical parameters were also assessed. Statistical tests included Mann-Whitney U, Pearson correlation, and linear regression. Results: In HCC patients, high baseline ETV% significantly correlated with high TNR on SPECT, demonstrating greater 90Y uptake in the tumor relative to the liver parenchyma (P < 0.001). In non-HCC patients, a correlation between ETV% and TNR was observed as well (P = 0.039). Follow-up imaging for response assessments within 1-4 mo after TARE was available for 23 patients with 25 treatments. The change of ETV% significantly correlated with TNR in HCC (P = 0.039) but not in non-HCC patients (P = 0.886). Additionally, Child-Pugh class B patients demonstrated significantly more 90Y deposition in nontumorous liver than Child-Pugh A patients (P = 0.021). Conclusion: This study identified ETV% as a quantifiable imaging biomarker on preprocedural MRI and CT to predict 90Y distribution on postprocedural SPECT in HCC and non-HCC. However, the relationship between the preferential uptake of 90Y to the tumor and tumor response after radioembolization could be validated only for HCC.

Three-dimensional quantitative assessment of lesion response to MR-guided high-intensity focused ultrasound treatment of uterine fibroids.

RATIONALE AND OBJECTIVES: To investigate the response after magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) treatment of uterine fibroids (UF) using a three-dimensional (3D) quantification of total and enhancing lesion volume (TLV and ELV, respectively) on contrast-enhanced MRI (ceMRI) scans. METHODS AND MATERIALS: In a total of 24 patients, ceMRI scans were obtained at baseline and 24 hours, and 6, 12, and 24 months after MRgHIFU treatment. The dominant lesion was assessed using a semiautomatic quantitative 3D segmentation technique. Agreement between software-assisted and manual measurements was then analyzed using a linear regression model. Patients were classified as responders (R) or nonresponders (NR) on the basis of their symptom report after 6 months. Statistical analysis included the paired t-test and Mann-Whitney test. RESULTS: Preprocedurally, the median TLV and ELV were 263.74 cm(3) (30.45-689.56 cm(3)) and 210.13 cm(3) (14.43-689.53 cm(3)), respectively. The 6-month follow-up demonstrated a reduction of TLV in 21 patients (87.5%) with a median TLV of 171.7 cm(3) (8.5-791.2 cm(3); P < .0001). TLV remained stable with significant differences compared to baseline (P < .001 and P = .047 after 12 and 24 months). A reduction of ELV was apparent in 16 patients (66.6%) with a median ELV of 158.91 cm(3) (8.55-779.61 cm(3)) after 6 months (P = .065). Three-dimensional quantification and manual measurements showed strong intermethod agreement for fibroid volumes (R(2) = .889 and .917) but greater discrepancy for enhancement calculations (R(2) = .659 and .419) at baseline and 6 months. No significant differences in TLV or ELV were observed between clinical R (n = 15) and NR (n = 3). CONCLUSIONS: The 3D assessment has proven feasible and accurate in the quantification of fibroid response to MRgHIFU. Contrary to ELV, changes in TLV may be representative of the clinical outcome.

Uveal Melanoma Metastatic to the Liver: The Role of Quantitative Volumetric Contrast-Enhanced MR Imaging in the Assessment of Early Tumor Response after Transarterial Chemoembolization.

PURPOSE: To determine whether volumetric changes of enhancement as seen on contrast-enhanced magnetic resonance (MR) imaging can help assess early tumor response and predict survival in patients with metastatic uveal melanoma after one session of transarterial chemoembolization (TACE). MATERIALS AND METHODS: Fifteen patients with 59 lesions who underwent MR imaging before and 3 to 4 weeks after the first TACE were retrospectively included. MR analysis evaluated signal intensities, World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), European Association for the Study of the Liver (EASL), modified RECIST (mRECIST), tumor volume [volumetric RECIST (vRECIST)], and volumetric tumor enhancement [quantitative EASL (qEASL)]. qEASL was expressed in cubic centimeters [qEASL (cm(3))] and as a percentage of the tumor volume [qEASL (%)]. Paired t test with its exact permutation distribution was used to compare measurements before and after TACE. The Kaplan-Meier method with the log-rank test was used to calculate overall survival for responders and non-responders. RESULTS: In target lesions, mean qEASL (%) decreased from 63.9% to 42.6% (P = .016). No significant changes were observed using the other response criteria. In non-target lesions, mean WHO, RECIST, EASL, mRECIST, vRECIST, and qEASL (cm(3)) were significantly increased compared to baseline. qEASL (%) remained stable (P = .214). Median overall survival was 5.6 months. qEASL (cm(3)) was the only parameter that could predict survival based on target lesions (3.6 vs 40.5 months, P < .001) or overall (target and non-target lesions) response (4.4 vs 40.9 months, P = .001). CONCLUSION: Volumetric tumor enhancement may be used as a surrogate biomarker for survival prediction in patients with uveal melanoma after the first TACE.