BACKGROUND:Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research. METHODS:1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed. The performance of defacing with the face mask created by TotalSegmentator was compared to a state-of-the-art CT defacing algorithm. Face detection was performed using deep learning models. The cosine similarity between facial embeddings for intra- and inter-patient images was compared. A Support Vector Machine was trained on cosine similarity values to assess defacing performance, determining if two renderings came from the same patient. This analysis was conducted on defaced and non-defaced images using 5-fold cross-validation. RESULTS:Faces were detected in 76.5 % of non-defaced images. Intra-patient images exhibited a median cosine similarity of 0.65 (IQR: 0.47-0.80), compared to 0.50 (IQR: 0.39-0.62) for inter-patient images. A binary classifier performed moderately on non-defaced images, achieving a ROC-AUC of 0.69 (SD = 0.01) and an accuracy of 0.65 (SD = 0.01) in distinguishing whether a scan belonged to the same or a different individual. Following defacing, performance declined markedly. Defacing with the TotalSegmentator decreased the ROC-AUC to 0.55 (SD = 0.02) and the accuracy to 0.56 (SD = 0.01), whereas the CTA-DEFACE algorithm brought the performance down to a ROC-AUC of 0.60 (SD = 0.02) and an accuracy of 0.59 (SD = 0.01). These results demonstrate the effectiveness of defacing algorithms in mitigating re-identification risks, with the TotalSegmentator providing slightly superior privacy protection. CONCLUSION:Facial recognition software can identify facial features from partial and complete head CT scan renderings. However, using the TotalSegmentator to deface images reduces re-identification risks to a near-chance level. We offer code to implement this privacy-preserving pipeline. IMPLICATIONS FOR PRACTICE:Utilizing the TotalSegmentator framework, the proposed pipeline efficiently removes facial features from CT images, making it ideal for multi-site research and data sharing. It is a useful tool for radiographers and radiologists who must comply with medico-legal requirements necessitating the removal of facial features.

Background: The Prostate Imaging-Reporting and Data System (PI-RADS) calls for reporting the prostate index lesion and the location within the transition (TZ) or peripheral zone (PZ) and location on a corresponding sector map. The aim of this study was to train a deep learning DL-based algorithm for automatic prostate sector mapping and to validate its' performance. Methods: An automatic 24-sector grid-map (ASG) of the prostate was developed, based on an automatic zone-specific deep learning segmentation of the prostate. To evaluate the efficacy of the method, fiducials for random locations within the prostate were placed, and the corresponding sectors were determined for 50 mpMRI datasets. The reference standard was defined in a consensus read by two expert uroradiologists. Annotated fiducial locations were evaluated automatically by the ASG and by four radiologists in two reads with and without the help of a superimposed sector grid-map and the success rate was compared. Results: The ASG algorithm identified the correct prostate sector of the annotated lesions in 80 % (40/50 reads) of the cases and outperformed readings of the four radiologists with 55 % (109/200), p < 0.0001. The added use of the 24 ASG map significantly improved the rate of correct sector annotation for the four radiologists to 71 % (141/200), p < 0.004. Conclusion: The 24 ASG map was effective for prostate sector segmentation and significantly improved location reporting of prostate lesions.

Importance:The magnetic resonance imaging (MRI) pathway for diagnosing clinically significant prostate cancer (csPCa; defined as International Society of Urological Pathology grade group ≥2) uses multiparametric MRI (mpMRI) for prostate biopsy (PB) decision-making. However, the intermediate impact on patient outcomes in men with negative MRI results avoiding PB and men with positive MRI results without PCa remains unknown. Objective:To assess the feasibility and safety of a community-based MRI diagnostic strategy in men with suspected PCa using 3-year active monitoring. Design, Setting, and Participants:This multisite, longitudinal cohort trial took place across 54 community-based urology practices and 2 radiology imaging centers at a referral academic institution in Berlin, Germany. Eligible participants aged 18 to 75 years with clinically suspected PCa were enrolled between September 2016 and December 2017 and monitored for 3 years. Final analysis was reported on December 23, 2023. Exposures:Participants underwent 3-T mpMRI. Men with findings suspected to be PCa were recommended for targeted PB (diagnostic phase). Men with negative mpMRI results or positive mpMRI results with benign findings at PB were systematically monitored for 3 years (monitoring phase). Clinical visits were recommended every 6 months. Main Outcomes and Measures:The total proportion of men avoiding PB and those with csPCa. Results:A total of 593 men (median [IQR] age, 64 [58-70] years) underwent mpMRI, with 286 (48%) having negative MRI results, 261 (44%) avoiding PB initially, and 242 (41%) avoiding PB over 3 years. csPCa was detected in 161 (27%) men after immediate PB, increasing to 172 (29%) men after 3 years. Seven men with negative MRI results were diagnosed with PCa by immediate PB (including 4 cases of csPCa), while 279 entered monitoring. Three-year monitoring was completed by 233 (84%) men, with 7 diagnoses of csPCa. Of 307 men with positive MRI results, 58 (19%) showed no PCa after immediate PB, of which 41 (71%) completed monitoring and 4 (7%) were diagnosed with csPCa. Conclusions and Relevance:In this cohort study, men with negative mpMRI results avoiding biopsy were not at elevated risk of csPCa. The study confirms the oncological safety of the prebiopsy MRI strategy of avoiding an immediate PB after negative MRI results when a programmatic safety net is in place.

To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS). 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. A total of 1604 patients aged 67 (interquartile range, 61–73) with 48

Background and objective: Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone. Methods: A total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration-cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2-5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group >= 2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test. Key findings and limitations: Overall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS- and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs 1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy. Conclusions and clinical implications: The tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences. Patient summary: The computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences. (c) 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Purpose:Histological microvascular invasion (MVI) is a risk factor for poor survival and early recurrence in hepatocellular carcinoma (HCC) after surgery. Its prognostic value in the setting of locoregional therapies (LRT), where no tissue samples are obtained, remains unknown. This study aims to establish CT-derived indices indicative of MVI on liver MRI with superior soft tissue contrast and evaluate their association with patient survival after ablation via interstitial brachytherapy (iBT) versus iBT combined with prior conventional transarterial chemoembolization (cTACE). Patients and Methods:Ninety-five consecutive patients, who underwent ablation via iBT alone (n = 47) or combined with cTACE (n = 48), were retrospectively included between 01/2016 and 12/2017. All patients received contrast-enhanced MRI prior to LRT. Overall (OS), progression-free survival (PFS), and time-to-progression (TTP) were assessed. Decision-tree models to determine Radiogenomic Venous Invasion (RVI) and Two-Trait Predictor of Venous Invasion (TTPVI) on baseline MRI were established, validated on an external test set (TCGA-LIHC), and applied in the study cohorts to investigate their prognostic value for patient survival. Statistics included Fisher's exact and t-test, Kaplan-Meier and cox-regression analysis, area under the receiver operating characteristic curve (AUC-ROC) and Pearson's correlation. Results:OS, PFS, and TTP were similar in both treatment groups. In the external dataset, RVI showed low sensitivity but relatively high specificity (AUC-ROC = 0.53), and TTPVI high sensitivity but only low specificity (AUC-ROC = 0.61) for histological MVI. In patients following iBT alone, positive RVI and TTPVI traits were associated with poorer OS (RVI: p < 0.01; TTPVI: p = 0.08), PFS (p = 0.04; p = 0.04), and TTP (p = 0.14; p = 0.03), respectively. However, when patients with combined cTACE and iBT were stratified by RVI or TTPVI, no differences in OS (p = 0.75; p = 0.55), PFS (p = 0.70; p = 0.43), or TTP (p = 0.33; p = 0.27) were observed. Conclusion:The study underscores the role of non-invasive imaging biomarkers indicative of MVI to identify patients, who would potentially benefit from embolotherapy via cTACE prior to ablation rather than ablation alone.

Purpose: The ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data. Method: A 3D CNN (3D-ResNet18) was trained using a dataset of 31,602 prostate MRI volumes with 10 different sequence types of 1243 patients. A five-fold cross-validation approach with patient-based splits was chosen for training and testing. Training was repeated with a gradual reduction in training data assessing classification accuracies to determine the minimum training data required for sufficient performance. The trained model and developed method were tested on three external datasets. Results: The model achieved an overall accuracy of 99.88 % & PLUSMN; 0.13 % in classifying typical prostate MRI sequence types. When being trained with approximately 10 % of the original cohort (112 patients), the CNN still achieved an accuracy of 97.43 % & PLUSMN; 2.10 %. In external testing the model achieved sensitivities of > 90 % for 10/ 15 tested sequence types. Conclusions: The herein developed CNN enabled automatic and reliable sequence identification in prostate MRI. Ultimately, such CNN models for voxel-based sequence identification could substantially enhance the management of medical imaging data, improve workflow efficiency and data quality, and allow for robust clinical AI workflows.

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.

Purpose: To identify disease-specific profiles comprising patient characteristics and imaging biomarkers on contrast-enhanced (CE)-computed tomography (CT) that enable the non-invasive prediction of the hepatopulmonary shunt fraction (HPSF) in patients with hepatocellular carcinoma (HCC) before resin-based transarterial radioembolization (TARE).Patients and Methods: This institutional review board-approved (EA2/071/19) retrospective study included 56 patients with HCC recommended for TARE. All patients received tri-phasic CE-CT within 6 weeks prior to an angiographic TARE evaluation study using technetium-99m macroaggregated albumin. Imaging biomarkers representative of tumor extent, morphology, and perfusion, as well as disease-specific clinical parameters, were used to perform data-driven variable selection with backward elimination to generate multivariable linear regression models predictive of HPSF.Results were used to create clinically applicable risk scores for patients scheduled for TARE. Additionally, Cox regression was used to identify independent risk factors for poor overall survival (OS). Results: Mean HPSF was 13.11% +/- 7.6% (range: 2.8- 35.97%). Index tumor diameter (p = 0.014) or volume (p = 0.034) in combination with index tumor non-rim arterial phase enhancement (APHE) (p < 0.001) and washout (p < 0.001) were identified as significant non-invasive predictors of HPSF on CE-CT. Specifically, the prediction models revealed that the HPSF increased with index lesion diameter or volume and showed higher HPSF if non-rim APHE was present. In contrast, index tumor washout was associated with decreased HPSF levels. Independent risk factors of poorer OS were radiogenomic venous invasion and ascites at baseline.Conclusion: The featured prediction models can be used for the initial non-invasive estimation of HPSF in patients with HCC before TARE to assist in clinical treatment evaluation while potentially sparing ineligible patients from the angiographic shunt evaluation study.

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.

Purpose To analyze possible differences in the inter-reader variability between PI-RADS version 2 (v2) and version 2.1 (v2.1) for the classification of prostate lesions using multiparametric MRI (mpMRI) of the prostate. Methods In this retrospective and randomized study, 239 annotated and histopathologically correlated prostate lesions (104 positive and 135 negative for prostate cancer) were rated twice by three experienced uroradiologists using PI-RADS v2 and v2.1 with an interval of at least two months between readings. Results were tabulated across readers and reading timepoints and inter-reader variability was determined using Fleiss' kappa (kappa). Thereafter, an additional analysis of the data was performed in which PI-RADS scores 1 and 2 were combined, as they have the same clinical consequences. Results PI-PI-RADS v2.1 showed better inter-reader agreement in the peripheral zone (PZ), but poorer inter-reader agreement in the transition zone (TZ) (PZ: kappa = 0.63 vs. kappa = 0.58; TZ: kappa = 0.47 vs. kappa = 0.57). When PI-RADS scores 1 and 2 were combined, the use of PI-RADS v2.1 resulted in almost perfect inter-reader agreement in the PZ and substantial agreement in the TZ (PZ: kappa = 0.81; TZ: kappa = 0.80). Conclusion PI-RADS v2.1 improves inter-reader agreement in the PZ. New differences in inter-reader agreement were mainly the result of the assignment of PI-RADS v2.1 scores 1 and 2 to lesions in the TZ. Combining scores 1 and 2 improved inter-reader agreement both in the TZ and in the PZ, indicating that refined definitions may be warranted for these PI-RADS scores. Citation Format Beetz N, Haas M, Baur A et al. Inter-Reader Variability Using PI-RADS v2 Versus PI-RADS v2.1: Most New Disagreement Stems from Scores 1 and 2. Fortschr Rontgenstr 2022; DOI: 10.1055/a-1752-1038

Purpose To analyze possible differences in the inter-reader variability between PI-RADS version 2 (v2) and version 2.1 (v2.1) for the classification of prostate lesions using multiparametric MRI (mpMRI) of the prostate. Methods In this retrospective and randomized

Background Magnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model. Methods We retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates. Results The most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model. Conclusion The most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates.

This study compared the efficacy and safety of conventional transarterial chemoembolization (cTACE) with drug-eluting beads (DEB)-TACE in patients with unresectable hepatocellular carcinoma (HCC). This retrospective analysis included 370 patients with HCC treated with cTACE (n = 248) or DEB-TACE (n = 122) (January 2000–July 2014). Overall survival (OS) was assessed using uni- and multivariate Cox proportional hazards models and Kaplan-Meier analysis. Additionally, baseline imaging was assessed, and clinical and laboratory toxicities were recorded. Propensity score weighting via a generalized boosted model was applied to account for group heterogeneity. There was no significant difference in OS between cTACE (20 months) and DEB-TACE patients (24.3 months, ratio 1.271, 95% confidence interval 0.876–1.69; p = 0.392). However, in patients with infiltrative disease, cTACE achieved longer OS (25.1 months) compared to DEB-TACE (9.2 months, ratio 0.366, 0.191–0.702; p = 0.003), whereas DEB-TACE proved more effective in nodular disease (39.4 months) than cTACE (18 months, ratio 0.458, 0.308–0681; p = 0.007). Adverse events occurred with similar frequency, except for abdominal pain, which was observed more frequently after DEB-TACE (101/116; 87.1%) than cTACE (119/157; 75.8%; p = 0.02). In conclusion, these findings suggest that tumor morphology and distribution should be used as parameters to inform decisions on the selection of embolic materials for TACE for a more personalized treatment planning in patients with unresectable HCC.

摘要目的建立一种基于深度学习模型以区分经病理证实的肝细胞癌（HCC）和非HCC病变,HCC包括MRI上具有不典型影像特征的病变。方法这项经伦理委员会批准的

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. 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. 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. 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. • 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).

Introduction: Given the metachronous and multifocal occurrence of hepatocellular carcinoma (HCC) and colorectal cancer metastases in the liver (CRLM), this study aimed to compare intrahepatic progression patterns after computed tomography (CT)-guided high dose-rate brachytherapy. Patients and methods: This retrospective analysis included 164 patients (114 HCC, 50 CRLM) treated with brachytherapy between January 2016 and January 2018. Patients received multiparametric magnetic resonance imaging (MRI) before, and about 8 weeks after brachytherapy, then every 3 months for the first, and every 6 months for the following years, until progression or death. MRI scans were assessed for local or distant intrahepatic tumor progression according to RECIST 1.1 and electronic medical records were reviewed prior to therapy. The primary endpoint was progression-free survival (PFS). Specifically, local and distant intra-hepatic PFS were assessed to determine differences between the intrahepatic progression patterns of HCC and CRLM. Secondary endpoints included the identification of predictors of PFS, time to progression (TTP), and overall survival (OS). Statistics included Kaplan–Meier analysis and univariate and multivariate Cox regression modeling. Results: PFS was longer in HCC [11.30 (1.33–35.37) months] than in CRLM patients [8.03 (0.73–19.80) months, p = 0.048], respectively. Specifically, local recurrence occurred later in HCC [PFS: 36.83 (1.33–40.27) months] than CRLM patients [PFS: 12.43 (0.73–21.90) months, p = 0.001]. In contrast, distant intrahepatic progression occurred earlier in HCC [PFS: 13.50 (1.33–27.80) months] than in CRLM patients [PFS: 19.80 (1.43–19.80) months, p = 0.456] but without statistical significance. Multivariate Cox regression confirmed tumor type and patient age as independent predictors for PFS. Conclusion: Brachytherapy proved to achieve better local tumor control and overall PFS in patients with unresectable HCC as compared to those with CRLM. However, distant progression preceded local recurrence in HCC. As a result, these findings may help design disease-specific surveillance strategies and personalized treatment planning that highlights the strengths of brachytherapy. They may also help elucidate the potential benefits of combinations with other loco-regional or systemic therapies.

Given the extraordinary nature of tumor metabolism in hepatocellular carcinoma and its impact on oncologic treatment response, this study introduces a novel high-throughput extracellular pH (pHe ) mapping platform using magnetic resonance spectroscopic imaging in a three-dimensional (3D) in vitro model of liver cancer. pHe mapping was performed using biosensor imaging of redundant deviation in shifts (BIRDS) on 9.4 T and 11.7 T MR scanners for validation purposes. 3D cultures of four liver cancer (HepG2, Huh7, SNU475, VX2) and one hepatocyte (THLE2) cell line were simultaneously analyzed (a) without treatment, (b) supplemented with 4.5 g/L d-glucose, and (c) treated with anti-glycolytic 3-bromopyruvate (6.25, 25, 50, 75, and 100 μM). The MR results were correlated with immunohistochemistry (GLUT-1, LAMP-2) and luminescence-based viability assays. Statistics included the unpaired t-test and ANOVA test. High-throughput pHe imaging with BIRDS for in vitro 3D liver cancer models proved feasible. Compared with non-tumorous hepatocytes (pHe = 7.1 ± 0.1), acidic pHe was revealed in liver cancer (VX2, pHe = 6.7 ± 0.1; HuH7, pHe = 6.8 ± 0.1; HepG2, pHe = 6.9 ± 0.1; SNU475, pHe = 6.9 ± 0.1), in agreement with GLUT-1 upregulation. Glucose addition significantly further decreased pHe in hyperglycolytic cell lines (VX2, HepG2, and Huh7, by 0.28, 0.06, and 0.11, respectively, all p < 0.001), whereas 3-bromopyruvate normalized tumor pHe in a dose-dependent manner without affecting viability. In summary, this study introduces a non-invasive pHe imaging platform for high-yield screening using a translational 3D liver cancer model, which may help reveal and target mechanisms of therapy resistance and inform personalized treatment of patients with hepatocellular carcinoma.

The purpose of this study is to compare diagnostic performance of Prostate Imaging Reporting and Data System (PI-RADS) version (v) 2.1 and 2.0 for detection of Gleason Score (GS) ≥ 7 prostate cancer on MRI. Three experienced radiologists provided PI-RADS v2.0 scores and at least 12 months later v2.1 scores on lesions in 333 prostate MRI examinations acquired between 2012 and 2015. Diagnostic performance was assessed retrospectively by using MRI/transrectal ultrasound fusion biopsy and 10-core systematic biopsy as the reference. From a total of 359 lesions, GS ≥ 7 tumor was present in 135 lesions (37.60%). Area under the ROC curve (AUC) revealed slightly lower values for peripheral zone (PZ) and transition zone (TZ) scoring in v2.1, but these differences did not reach statistical significance. A significant number of score 2 lesions in the TZ were downgraded to score 1 in v2.1 showing 0% GS ≥ 7 tumor (0/11). The newly introduced diffusion-weighted imaging (DWI) upgrading rule in v2.1 was applied in 6 lesions from a total of 143 TZ lesions (4.2%). In summary, PI-RADS v2.1 showed no statistically significant differences in overall diagnostic performance of TZ and PZ scoring compared to v2.0. Downgraded BPH nodules showed favorable cancer frequencies. The new DWI upgrading rule for TZ lesions was applied in only few cases.

Traditional palaeontological techniques of disease characterisation are limited to the analysis of osseous fossils, requiring several lines of evidence to support diagnoses. This study presents a novel stepwise concept for comprehensive diagnosis of pathologies in fossils by computed tomography imaging for morphological assessment combined with likelihood estimation based on systematic phylogenetic disease bracketing. This approach was applied to characterise pathologies of the left fibula and fused caudal vertebrae of the non-avian dinosaur Tyrannosaurus rex. Initial morphological assessment narrowed the differential diagnosis to neoplasia or infection. Subsequent data review from phylogenetically closely related species at the clade level revealed neoplasia rates as low as 3.1% and 1.8%, while infectious-disease rates were 32.0% and 53.9% in extant dinosaurs (birds) and non-avian reptiles, respectively. Furthermore, the survey of literature revealed that within the phylogenetic disease bracket the oldest case of bone infection (osteomyelitis) was identified in the mandible of a 275-million-year-old captorhinid eureptile Labidosaurus. These findings demonstrate low probability of a neoplastic aetiology of the examined pathologies in the Tyrannosaurus rex and in turn, suggest that they correspond to multiple foci of osteomyelitis.