Prostate MR image quality of apparent diffusion coefficient maps versus fractional intracellular volume maps from VERDICT MRI using the PI-QUAL score and a dedicated Likert scale for artefacts.

PURPOSE: This study aimed to assess the image quality of apparent diffusion coefficient (ADC) maps derived from conventional diffusion-weighted MRI and fractional intracellular volume maps (FIC) from VERDICT MRI (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) in patients from the INNOVATE trial. The inter-reader agreement was also assessed. METHODS: Two readers analysed both ADC and FIC maps from 57 patients enrolled in the INNOVATE prospective trial. Image quality was assessed using the Prostate Imaging Quality (PI-QUAL) score and a subjective image quality Likert score (Likert-IQ). The image quality of FIC and ADC were compared using a Wilcoxon Signed Ranks test. The inter-reader agreement was assessed with Cohen's kappa. RESULTS: There was no statistically significant difference between the PI-QUAL score for FIC datasets compared to ADC datasets for either reader (p = 0.240 and p = 0.614). Using the Likert-IQ score, FIC image quality was higher compared to ADC (p = 0.021) as assessed by reader-1 but not for reader-2 (p = 0.663). The inter-reader agreement was 'fair' for PI-QUAL scoring of datasets with FIC maps at 0.27 (95% confidence interval; 0.08-0.46) and ADC datasets at 0.39 (95% confidence interval 0.22-0.57). For Likert scoring, the inter-reader agreement was also 'fair' for FIC maps at 0.38 (95% confidence interval; 0.10-0.65) and substantial for ADC maps at 0.62 (95% confidence interval; 0.39-0.86). CONCLUSION: Image quality was comparable for FIC and ADC. The inter-reader agreement was similar when using PIQUAL for both FIC and ADC datasets but higher for ADC maps compared to FIC maps using the image quality Likert score.

ST6GAL1-mediated aberrant sialylation promotes prostate cancer progression.

Aberrant glycosylation is a universal feature of cancer cells, and cancer-associated glycans have been detected in virtually every cancer type. A common change in tumour cell glycosylation is an increase in α2,6 sialylation of N-glycans, a modification driven by the sialyltransferase ST6GAL1. ST6GAL1 is overexpressed in numerous cancer types, and sialylated glycans are fundamental for tumour growth, metastasis, immune evasion, and drug resistance, but the role of ST6GAL1 in prostate cancer is poorly understood. Here, we analyse matched cancer and normal tissue samples from 200 patients and verify that ST6GAL1 is upregulated in prostate cancer tissue. Using MALDI imaging mass spectrometry (MALDI-IMS), we identify larger branched α2,6 sialylated N-glycans that show specificity to prostate tumour tissue. We also monitored ST6GAL1 in plasma samples from >400 patients and reveal ST6GAL1 levels are significantly increased in the blood of men with prostate cancer. Using both in vitro and in vivo studies, we demonstrate that ST6GAL1 promotes prostate tumour growth and invasion. Our findings show ST6GAL1 introduces α2,6 sialylated N-glycans on prostate cancer cells and raise the possibility that prostate cancer cells can secrete active ST6GAL1 enzyme capable of remodelling glycans on the surface of other cells. Furthermore, we find α2,6 sialylated N-glycans expressed by prostate cancer cells can be targeted using the sialyltransferase inhibitor P-3FAX -Neu5Ac. Our study identifies an important role for ST6GAL1 and α2,6 sialylated N-glycans in prostate cancer progression and highlights the opportunity to inhibit abnormal sialylation for the development of new prostate cancer therapeutics. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer: A Diagnostic Test Accuracy Study.

OBJECTIVES: Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times. MATERIALS AND METHODS: A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated. RESULTS: Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker ( P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML). CONCLUSIONS: There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support.

Robotic versus freehand CT-guided radiofrequency ablation of pulmonary metastases: a comparative cohort study.

PURPOSE: Radiofrequency ablation (RFA) is a curative treatment option for small lung metastases, which conventionally involves multiple freehand manipulations until the treating electrode is satisfactorily positioned. Stereotactic and robotic guidance has been gaining popularity for liver ablation, although has not been established in lung ablation. The purpose of this study is to determine the feasibility, safety, and accuracy of robotic RFA for pulmonary metastases, and compare procedures with a conventional freehand cohort. METHODS: A single center study with prospective robotic cohort, and retrospective freehand cohort. RFA was performed under general anesthesia using high frequency jet ventilation and CT guidance. Main outcomes were (i) feasibility/technical success (ii) safety using Common Terminology Criteria for Adverse Events (iii) targeting accuracy (iv) number of needle manipulations for satisfactory ablation. Robotic and freehand cohorts were compared using Mann-Whitney U tests for continuous variables, and Fisher's exact for categorical variables. RESULTS: Thirty-nine patients (mean age 65 ± 13 years, 20 men) underwent ablation of 44 pulmonary metastases at single specialist cancer center between July 2019 and August 2022. 20 consecutive participants underwent robotic ablation, and 20 consecutive patients underwent freehand ablation. All 20/20 (100%) robotic procedures were technically successful, and none were converted to freehand procedures. There were 6/20 (30%) adverse events in the robotic cohort, and 15/20 (75%) in the freehand cohort (P = 0.01). Robotic placement was highly accurate with 6 mm tip-to-target distance (range 0-14 mm) despite out-of-plane approaches, with fewer manipulations than freehand placement (median 0 vs. 4.5 manipulations, P < 0.001 and 7/22, 32% vs. 22/22, 100%, P < 0.001). CONCLUSIONS: Robotic radiofrequency ablation of pulmonary metastases with general anesthesia and high frequency jet ventilation is feasible and safe. Targeting accuracy is high, and fewer needle/electrode manipulations are required to achieve a satisfactory position for ablation than freehand placement, with early indications of reduced complications.

Upregulation of GALNT7 in prostate cancer modifies O-glycosylation and promotes tumour growth.

Prostate cancer is the most common cancer in men and it is estimated that over 350,000 men worldwide die of prostate cancer every year. There remains an unmet clinical need to improve how clinically significant prostate cancer is diagnosed and develop new treatments for advanced disease. Aberrant glycosylation is a hallmark of cancer implicated in tumour growth, metastasis, and immune evasion. One of the key drivers of aberrant glycosylation is the dysregulated expression of glycosylation enzymes within the cancer cell. Here, we demonstrate using multiple independent clinical cohorts that the glycosyltransferase enzyme GALNT7 is upregulated in prostate cancer tissue. We show GALNT7 can identify men with prostate cancer, using urine and blood samples, with improved diagnostic accuracy than serum PSA alone. We also show that GALNT7 levels remain high in progression to castrate-resistant disease, and using in vitro and in vivo models, reveal that GALNT7 promotes prostate tumour growth. Mechanistically, GALNT7 can modify O-glycosylation in prostate cancer cells and correlates with cell cycle and immune signalling pathways. Our study provides a new biomarker to aid the diagnosis of clinically significant disease and cements GALNT7-mediated O-glycosylation as an important driver of prostate cancer progression.

Developing and testing a robotic MRI/CT fusion biopsy technique using a purpose-built interventional phantom.

BACKGROUND: Magnetic resonance imaging (MRI) can be used to target tumour components in biopsy procedures, while the ability to precisely correlate histology and MRI signal is crucial for imaging biomarker validation. Robotic MRI/computed tomography (CT) fusion biopsy offers the potential for this without in-gantry biopsy, although requires development. METHODS: Test-retest T1 and T2 relaxation times, attenuation (Hounsfield units, HU), and biopsy core quality were prospectively assessed (January-December 2021) in a range of gelatin, agar, and mixed gelatin/agar solutions of differing concentrations on days 1 and 8 after manufacture. Suitable materials were chosen, and four biopsy phantoms were constructed with twelve spherical 1-3-cm diameter targets visible on MRI, but not on CT. A technical pipeline was developed, and intraoperator and interoperator reliability was tested in four operators performing a total of 96 biopsies. Statistical analysis included T1, T2, and HU repeatability using Bland-Altman analysis, Dice similarity coefficient (DSC), and intraoperator and interoperator reliability. RESULTS: T1, T2, and HU repeatability had 95% limits-of-agreement of 8.3%, 3.4%, and 17.9%, respectively. The phantom was highly reproducible, with DSC of 0.93 versus 0.92 for scanning the same or two different phantoms, respectively. Hit rate was 100% (96/96 targets), and all operators performed robotic biopsies using a single volumetric acquisition. The fastest procedure time was 32 min for all 12 targets. CONCLUSIONS: A reproducible biopsy phantom was developed, validated, and used to test robotic MRI/CT-fusion biopsy. The technique was highly accurate, reliable, and achievable in clinically acceptable timescales meaning it is suitable for clinical application.

Avoiding Unnecessary Biopsy after Multiparametric Prostate MRI with VERDICT Analysis: The INNOVATE Study.

Background In men suspected of having prostate cancer (PCa), up to 50% of men with positive multiparametric MRI (mpMRI) findings (Prostate Imaging Reporting and Data System [PI-RADS] or Likert score of 3 or higher) have no clinically significant (Gleason score ≤3+3, benign) biopsy findings. Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumor (VERDICT) MRI analysis could improve the stratification of positive mpMRI findings. Purpose To evaluate VERDICT MRI, mpMRI-derived apparent diffusion coefficient (ADC), and prostate-specific antigen density (PSAD) as determinants of clinically significant PCa (csPCa). Materials and Methods Between April 2016 and December 2019, men suspected of having PCa were prospectively recruited from two centers and underwent VERDICT MRI and mpMRI at one center before undergoing targeted biopsy. Biopsied lesion ADC, lesion-derived fractional intracellular volume (FIC), and PSAD were compared between men with csPCa and those without csPCa, using nonparametric tests subdivided by Likert scores. Area under the receiver operating characteristic curve (AUC) was calculated to test diagnostic performance. Results Among 303 biopsy-naive men, 165 study participants (mean age, 65 years ± 7 [SD]) underwent targeted biopsy; of these, 73 had csPCa. Median lesion FIC was higher in men with csPCa (FIC, 0.53) than in those without csPCa (FIC, 0.18) for Likert 3 (P = .002) and Likert 4 (0.60 vs 0.28, P < .001) lesions. Median lesion ADC was lower for Likert 4 lesions with csPCa (0.86 × 10-3 mm2/sec) compared with lesions without csPCa (1.12 × 10-3 mm2/sec, P = .03), but there was no evidence of a difference for Likert 3 lesions (0.97 × 10-3 mm2/sec vs 1.20 × 10-3 mm2/sec, P = .09). PSAD also showed no difference for Likert 3 (0.17 ng/mL2 vs 0.12 ng/mL2, P = .07) or Likert 4 (0.14 ng/mL2 vs 0.12 ng/mL2, P = .47) lesions. The diagnostic performance of FIC (AUC, 0.96; 95% CI: 0.93, 1.00) was higher (P = .02) than that of ADC (AUC, 0.85; 95% CI: 0.79, 0.91) and PSAD (AUC, 0.74; 95% CI: 0.66, 0.82) for the presence of csPCa in biopsied lesions. Conclusion Lesion fractional intracellular volume enabled better classification of clinically significant prostate cancer than did apparent diffusion coefficient and prostate-specific antigen density. Clinical trial registration no. NCT02689271 © RSNA, 2022 Online supplemental material is available for this article.

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.

Starting CT-guided robotic interventional oncology at a UK centre.

OBJECTIVE: A commercially available CT-guided robot offers enhanced abilities in planning, targeting, and confirming accurate needle placement. In this short communication, we describe our first UK experience of robotic interventional oncology procedures. METHODS: We describe the device, discuss installation, operation, and report upon needle insertion success, accuracy (path deviation; PD and tip deviation; TD), number of adjustments, complications, and procedural success. RESULTS: Nine patients (seven males), median age 66 years (range 43-79) were consented for biopsy or ablation between March and April 2021. Needle placement in biopsy was more accurate than ablation (median 1 vs 11 mm PD and 1 vs 20 mm TD) and required fewer adjustments (median 0 vs 5). No complications arose, and all procedures were successful (diagnostic material obtained or complete ablation at follow-up). CONCLUSION: Short procedure times and very high levels of accuracy were readily achieved with biopsy procedures, although tumour ablation was less accurate which likely reflects higher procedural complexity. ADVANCES IN KNOWLEDGE: Achieving highly accurate robotic biopsy with is feasible within a very short time span. Further work is required to maximise the potential of robotic guidance in tumour ablation procedures, which is likely due to higher complexity giving a longer learning curve.

AutoProstate: Towards Automated Reporting of Prostate MRI for Prostate Cancer Assessment Using Deep Learning.

Multiparametric magnetic resonance imaging (mpMRI) of the prostate is used by radiologists to identify, score, and stage abnormalities that may correspond to clinically significant prostate cancer (CSPCa). Automatic assessment of prostate mpMRI using artificial intelligence algorithms may facilitate a reduction in missed cancers and unnecessary biopsies, an increase in inter-observer agreement between radiologists, and an improvement in reporting quality. In this work, we introduce AutoProstate, a deep learning-powered framework for automatic MRI-based prostate cancer assessment. AutoProstate comprises of three modules: Zone-Segmenter, CSPCa-Segmenter, and Report-Generator. Zone-Segmenter segments the prostatic zones on T2-weighted imaging, CSPCa-Segmenter detects and segments CSPCa lesions using biparametric MRI, and Report-Generator generates an automatic web-based report containing four sections: Patient Details, Prostate Size and PSA Density, Clinically Significant Lesion Candidates, and Findings Summary. In our experiment, AutoProstate was trained using the publicly available PROSTATEx dataset, and externally validated using the PICTURE dataset. Moreover, the performance of AutoProstate was compared to the performance of an experienced radiologist who prospectively read PICTURE dataset cases. In comparison to the radiologist, AutoProstate showed statistically significant improvements in prostate volume and prostate-specific antigen density estimation. Furthermore, AutoProstate matched the CSPCa lesion detection sensitivity of the radiologist, which is paramount, but produced more false positive detections.

Evaluation of PSA and PSA Density in a Multiparametric Magnetic Resonance Imaging-Directed Diagnostic Pathway for Suspected Prostate Cancer: The INNOVATE Trial.

Objectives: To assess the clinical outcomes of mpMRI before biopsy and evaluate the space remaining for novel biomarkers. Methods: The INNOVATE study was set up to evaluate the validity of novel fluidic biomarkers in men with suspected prostate cancer who undergo pre-biopsy mpMRI. We report the characteristics of this clinical cohort, the distribution of clinical serum biomarkers, PSA and PSA density (PSAD), and compare the mpMRI Likert scoring system to the Prostate Imaging-Reporting and Data System v2.1 (PI-RADS) in men undergoing biopsy. Results: 340 men underwent mpMRI to evaluate suspected prostate cancer. 193/340 (57%) men had subsequent MRI-targeted prostate biopsy. Clinically significant prostate cancer (csigPCa), i.e., overall Gleason ≥ 3 + 4 of any length OR maximum cancer core length (MCCL) ≥4 mm of any grade including any 3 + 3, was found in 96/195 (49%) of biopsied patients. Median PSA (and PSAD) was 4.7 (0.20), 8.0 (0.17), and 9.7 (0.31) ng/mL (ng/mL/mL) in mpMRI scored Likert 3,4,5 respectively for men with csigPCa on biopsy. The space for novel biomarkers was shown to be within the group of men with mpMRI scored Likert3 (178/340) and 4 (70/350), in whom an additional of 40% (70/178) men with mpMRI-scored Likert3, and 37% (26/70) Likert4 could have been spared biopsy. PSAD is already considered clinically in this cohort to risk stratify patients for biopsy, despite this 67% (55/82) of men with mpMRI-scored Likert3, and 55% (36/65) Likert4, who underwent prostate biopsy had a PSAD below a clinical threshold of 0.15 (or 0.12 for men aged <50 years). Different thresholds of PSA and PSAD were assessed in mpMRI-scored Likert4 to predict csigPCa on biopsy, to achieve false negative levels of ≤5% the proportion of patients whom who test as above the threshold were unsuitably high at 86 and 92% of patients for PSAD and PSA respectively. When PSA was re tested in a sub cohort of men repeated PSAD showed its poor reproducibility with 43% (41/95) of patients being reclassified. After PI-RADS rescoring of the biopsied lesions, 66% (54/82) of the Likert3 lesions received a different PI-RADS score. Conclusions: The addition of simple biochemical and radiological markers (Likert and PSAD) facilitate the streamlining of the mpMRI-diagnostic pathway for suspected prostate cancer but there remains scope for improvement, in the introduction of novel biomarkers for risk assessment in Likert3 and 4 patients, future application of novel biomarkers tested in a Likert cohort would also require re-optimization around Likert3/PI-RADS2, as well as reproducibility testing.

Stereotactic radiofrequency ablation (SRFA) for recurrent colorectal liver metastases after hepatic resection.

BACKGROUND: To evaluate the efficacy, safety and overall clinical outcome of multiprobe SRFA as a treatment for recurrent colorectal liver metastases after hepatic resection (HR). METHODS: A retrospective, single center study carried out between 2006 and 2018. 64 consecutive patients with recurrent or new CRLM after previous HR were treated by SRFA for 217 lesions (median size 2.7 cm, 1-7.5) in 103 ablation sessions. Endpoints consisted of i) technical efficacy ii) complication and mortality rates iii) local and distant recurrence, iv) disease free survival (DFS), and v) overall survival (OS). RESULTS: 213/217 tumors were successfully ablated at initial SRFA (97.7% primary technical efficacy rate). Four tumors required repeat ablation, resulting in a secondary technical efficacy rate of 99.5% (216/217). Local recurrence developed in 25/217 lesions (11.5%). Major complication rate was 5.8% (6/103 sessions) and mortality rate was 1.0% (1/103 ablation sessions), respectively.1-, 3-, and 5- year OS rates from date of first SRFA were 90.1%, 46.2%, and 34.8% (median 33.1 months). DFS rates were 54.2%, 17.2%, and 17.2%, at 1-, 3- and 5- years, respectively (median 13.3 months). CONCLUSION: SRFA is a safe, feasible and effective option for CRLM after HR with low morbidity levels and favorable clinical outcome.

Safety and efficacy of stereotactic radiofrequency ablation for very large (≥8 cm) primary and metastatic liver tumors.

To assess the safety and clinical outcomes of multi-probe stereotactic radiofrequency ablation (SRFA) for very large (≥8 cm) primary and metastatic liver tumors with curative treatment intent. A retrospective, single center study carried out between 01.2005 and 06.2018. 34 consecutive patients had a total of 41 primary and metastatic liver tumors with a median size of 9.0 cm (8.0-18.0 cm) at initial SRFA. Patients were treated under CT guidance using a 3D navigation system. Endpoints consisted of (i) technical efficacy; primary - requiring one treatment, and secondary - requiring two treatments (ii) complication and mortality rates (iii) local and distant recurrence (LR), (iv) disease free survival (DFS), (v) overall survival (OS). 33/41 tumors were successfully ablated at initial SRFA (80.5% primary technical efficacy rate (PTE)). Four tumors required repeat ablation, resulting in a secondary technical efficacy (STE) rate of 90.2%. Local tumor recurrence (LR) developed in 4 of 41 tumors (9.8%). The 30-day perioperative mortality was 2.3% (1/ 44 ablations). The total major complication rate was 20.5% (9 of 44 ablations). Three of nine (33.3%) major complications, such as pleural effusion, pneumothoraces or perihepatic hemorrhages were relatively easy to treat. The overall survival (OS) rates at 1-, 3-, and 5- years from the date of the first SRFA were 87.1%, 71.8%, and 62.8% for patients with hepatocellular carcinoma (HCC) and 87.5%, 70.0% and 70.0% for patients with intrahepatic cholangiocarcinoma (ICC) respectively. Patients with metastatic disease had OS rates of 77.8% and 22.2% at 1- and 3- years. The clinical results of SRFA in this study are encouraging and warrant a prospective multicenter study. SRFA may become one of the best therapeutic choices for a growing number of patients with primary and metastatic liver cancer.

Correction to: Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.

The original version of this article, published on 11 June 2019, unfortunately contained a mistake. The following correction has therefore been made in the original: In section "Multiparametric MRI review," the readers mentioned in the first sentence were partly incorrect.

Stereotactic radiofrequency ablation of subcardiac hepatocellular carcinoma: a case-control study.

Purpose: The purpose of this study is to evaluate the safety and efficacy of stereotactic radiofrequency ablation (SRFA) for the treatment of subcardiac hepatocellular carcinoma (HCC). Material and methods: From 2003 to 2018, 79 consecutive patients underwent 104 multi-probe SRFA sessions for the treatment of 114 subcardiac HCC with a median size of 2.5 cm (0.5-9.5 cm). The results were compared with a randomly selected control group of 79 patients with 242 HCC with a median size of 2.0 cm (0.5-12 cm) following SRFA in other (non-subcardiac) locations with propensity score matching. Results: The 95.6% of the tumors were successfully treated by the first ablation session (primary technical efficacy rate) and 99.1% after the second session (secondary technical efficacy rate). Local tumor recurrence developed in 8 nodules (7.0%). Major complication and perioperative mortality rates were 7.7% (8/104) and 1% (1/104), respectively. The overall survival (OS) rates from the date of the first SRFA with single subcardiac HCCs were 92%, 77% and 65% at 1, 3 and 5 years, respectively, with a median OS of 90.6 months. The disease-free survival (DFS) after SRFA was 75, 34 and 34%, at 1, 3 and 5 years, respectively, with a median DFS of 19.1 months. There was no statistically significant difference with the control group in terms of local tumor control, safety, OS and DFS. Conclusion: SRFA of subcardiac tumors is as safe and efficacious as when treating tumors remote from the heart.

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.

OBJECTIVE: The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists. METHODS: A retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists. RESULTS: The best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82). CONCLUSIONS: Machine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists. KEY POINTS: • Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

Diagnostic accuracy of whole-body MRI versus standard imaging pathways for metastatic disease in newly diagnosed colorectal cancer: the prospective Streamline C trial.

BACKGROUND: Whole-body MRI (WB-MRI) could be an alternative to multimodality staging of colorectal cancer, but its diagnostic accuracy, effect on staging times, number of tests needed, cost, and effect on treatment decisions are unknown. We aimed to prospectively compare the diagnostic accuracy and efficiency of WB-MRI-based staging pathways with standard pathways in colorectal cancer. METHODS: The Streamline C trial was a prospective, multicentre trial done in 16 hospitals in England. Eligible patients were 18 years or older, with newly diagnosed colorectal cancer. Exclusion criteria were severe systemic disease, pregnancy, contraindications to MRI, or polyp cancer. Patients underwent WB-MRI, the result of which was withheld until standard staging investigations were complete and the first treatment decision made. The multidisciplinary team recorded its treatment decision based on standard investigations, then on the WB-MRI staging pathway (WB-MRI plus additional tests generated), and finally on all tests. The primary outcome was difference in per-patient sensitivity for metastases between standard and WB-MRI staging pathways against a consensus reference standard at 12 months, in the per-protocol population. Secondary outcomes were difference in per-patient specificity for metastatic disease detection between standard and WB-MRI staging pathways, differences in treatment decisions, staging efficiency (time taken, test number, and costs), and per-organ sensitivity and specificity for metastases and per-patient agreement for local T and N stage. This trial is registered with the International Standard Randomised Controlled Trial registry, number ISRCTN43958015, and is complete. FINDINGS: Between March 26, 2013, and Aug 19, 2016, 1020 patients were screened for eligibility. 370 patients were recruited, 299 of whom completed the trial; 68 (23%) had metastasis at baseline. Pathway sensitivity was 67% (95% CI 56 to 78) for WB-MRI and 63% (51 to 74) for standard pathways, a difference in sensitivity of 4% (-5 to 13, p=0·51). No adverse events related to imaging were reported. Specificity did not differ between WB-MRI (95% [95% CI 92-97]) and standard pathways (93% [90-96], p=0·48). Agreement with the multidisciplinary team's final treatment decision was 96% for WB-MRI and 95% for the standard pathway. Time to complete staging was shorter for WB-MRI (median, 8 days [IQR 6-9]) than for the standard pathway (13 days [11-15]); a 5-day (3-7) difference. WB-MRI required fewer tests (median, one [95% CI 1 to 1]) than did standard pathways (two [2 to 2]), a difference of one (1 to 1). Mean per-patient staging costs were £216 (95% CI 211-221) for WB-MRI and £285 (260-310) for standard pathways. INTERPRETATION: WB-MRI staging pathways have similar accuracy to standard pathways and reduce the number of tests needed, staging time, and cost. FUNDING: UK National Institute for Health Research.