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Background: The study purpose was to develop a machine learning (ML)-based predictive model for event-free survival (EFS) in patients with hepatocellular carcinoma (HCC) undergoing stereotactic ablative radiotherapy (SABR). Methods: Patients receiving SABR for HCC at a single institution, between 2017 and 2020, were included in the study. They were split into training and test (85%:15%) cohorts. Events of interest were HCC recurrence or death. Three ML models were trained, the features were selected, and the hyperparameters were tuned. The performance was measured using Harrell’s C index with the best-performing model being tested on the unseen cohort. Results: Overall, 41 patients were included (training = 34, test = 7) and 64 lesions were analysed (training = 50, test = 14), resulting in 30 events (60% rate) in the training set (death = 6, recurrence = 24) and 8 events (57% rate) in the test set (death = 5, recurrence = 3). A Cox regression model, using age at treatment, albumin, and intra-lesional fat identified through MRI as variables, had the best performance with a mean training score of 0.78 (standard deviation (SD) 0.02), a mean validation of 0.78 (SD 0.18), and a test score of 0.94. Conclusions: Predicting the outcomes in patients with HCC, following SABR, using a novel model is feasible and warrants further evaluation.

Background: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis in cardiovascular disease has largely focused on CT and MRI modalities. This scoping review aims to summarise the existing literature on radiomic analysis techniques in cardiovascular disease. Methods: MEDLINE and Embase databases were searched for eligible studies evaluating radiomic techniques in living human subjects derived from CT, MRI or PET imaging investigating atherosclerotic disease. Data on study population, imaging characteristics and radiomics methodology were extracted. Results: Twenty-nine studies consisting of 5753 patients (3752 males) were identified, and 78.7% of patients were from coronary artery studies. Twenty-seven studies employed CT imaging (19 CT carotid angiography and 6 CT coronary angiography (CTCA)), and two studies studied PET/CT. Manual segmentation was most frequently undertaken. Processing techniques included voxel discretisation, voxel resampling and filtration. Various shape, first-order, second-order and higher-order radiomic features were extracted. Logistic regression was most commonly used for machine learning. Conclusion: Most published evidence was feasibility/proof of concept work. There was significant heterogeneity in image acquisition, segmentation techniques, processing and analysis between studies. There is a need for the implementation of standardised imaging acquisition protocols, adherence to published reporting guidelines and economic evaluation.

Abstract Objectives The study aim was to conduct a systematic review of the literature reporting the application of radiomics to imaging techniques in patients with ovarian lesions. Methods MEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS) was utilised to assess radiomic methodology. Results After duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications, 10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias. The highest RQS achieved was 61.1%, and the lowest was − 16.7%. Conclusion Radiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical translation. Clinical relevance statement Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical translation. Key points • Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses. • Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. • Modelling with larger cohorts and real-world evaluation is required before clinical translation. Graphical Abstract

Glioblastoma (GBM) has the typical radiological appearance (TRA) of a centrally necrotic, peripherally enhancing tumor with surrounding edema. The objective of this study was to determine whether the developing GBM displays a spectrum of imaging changes detectable on routine clinical imaging prior to TRA GBM. Patients with pre-operative imaging diagnosed with GBM (1 January 2014–31 March 2022) were identified from a neuroscience center. The imaging was reviewed by an experienced neuroradiologist. Imaging patterns preceding TRA GBM were analyzed. A total of 76 out of 555 (14%) patients had imaging preceding TRA GBM, 57 had solitary lesions, and 19 had multiple lesions (total = 84 lesions). Here, 83% of the lesions had cortical or cortical/subcortical locations. The earliest imaging features for 84 lesions were T2 hyperintensity/CT low density (n = 18), CT hyperdensity (n = 51), and T2 iso-intensity (n = 15). Lesions initially showing T2 hyperintensity/CT low density later showed T2 iso-intensity. When CT and MRI were available, all CT hyperdense lesions showed T2 iso-intensity, reduced diffusivity, and the following enhancement patterns: nodular 35%, solid 29%, none 26%, and patchy peripheral 10%. The mean time to develop TRA GBM from T2 hyperintensity was 140 days and from CT hyperdensity was 69 days. This research suggests that the developing GBM shows a spectrum of imaging features, progressing through T2 hyperintensity to CT hyperdensity, T2 iso-intensity, reduced diffusivity, and variable enhancement to TRA GBM. Red flags for non-TRA GBM lesions are cortical/subcortical CT hyperdense/T2 iso-intense/low ADC. Future research correlating this imaging spectrum with pathophysiology may provide insight into GBM growth patterns.

BackgroundFluorine-18 fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) is widely used for staging high-grade lymphoma, with the time to evaluate such studies varying depending on the complexity of the case. Integrating artificial intelligence (AI) within the reporting workflow has the potential to improve quality and efficiency. The aims of the present study were to evaluate the influence of an integrated research prototype segmentation tool implemented within diagnostic PET/CT reading software on the speed and quality of reporting with variable levels of experience, and to assess the effect of the AI-assisted workflow on reader confidence and whether this tool influenced reporting behaviour.MethodsNine blinded reporters (three trainees, three junior consultants and three senior consultants) from three UK centres participated in a two-part reader study. A total of 15 lymphoma staging PET/CT scans were evaluated twice: first, using a standard PET/CT reporting workflow; then, after a 6-week gap, with AI assistance incorporating pre-segmentation of disease sites within the reading software. An even split of PET/CT segmentations with gold standard (GS), false-positive (FP) over-contour or false-negative (FN) under-contour were provided. The read duration was calculated using file logs, while the report quality was independently assessed by two radiologists with >15 years of experience. Confidence in AI assistance and identification of disease was assessed via online questionnaires for each case.ResultsThere was a significant decrease in time between non-AI and AI-assisted reads (median 15.0 vs. 13.3 min, p

Introduction In a small percentage of patients, pulmonary nodules found on CT scans are early lung cancers. Lung cancer detected at an early stage has a much better prognosis. The British Thoracic Society guideline on managing pulmonary nodules recommends using multivariable malignancy risk prediction models to assist in management. While these guidelines seem to be effective in clinical practice, recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models.Methods and analysis This study is a prospective, observational multicentre study to assess the clinical utility of an AI-assisted CT-based lung cancer prediction tool (LCP) for managing incidental solid and part solid pulmonary nodule patients vs standard care. Two thousand patients will be recruited from 12 different UK hospitals. The primary outcome is the difference between standard care and LCP-guided care in terms of the rate of benign nodules and patients with cancer discharged straight after the assessment of the baseline CT scan. Secondary outcomes investigate adherence to clinical guidelines, other measures of changes to clinical management, patient outcomes and cost-effectiveness.Ethics and dissemination This study has been reviewed and given a favourable opinion by the South Central—Oxford C Research Ethics Committee in UK (REC reference number: 22/SC/0142).Study results will be available publicly following peer-reviewed publication in open-access journals. A patient and public involvement group workshop is planned before the study results are available to discuss best methods to disseminate the results. Study results will also be fed back to participating organisations to inform training and procurement activities.Trial registration number NCT05389774.

Abstract Background Anal cancer is a rare cancer with rising incidence. Despite the relatively good outcomes conferred by state-of-the-art chemoradiotherapy, further improving disease control and reducing toxicity has proven challenging. Developing and validating prognostic models using routinely collected data may provide new insights for treatment development and selection. However, due to the rarity of the cancer, it can be difficult to obtain sufficient data, especially from single centres, to develop and validate robust models. Moreover, multi-centre model development is hampered by ethical barriers and data protection regulations that often limit accessibility to patient data. Distributed (or federated) learning allows models to be developed using data from multiple centres without any individual-level patient data leaving the originating centre, therefore preserving patient data privacy. This work builds on the proof-of-concept three-centre atomCAT1 study and describes the protocol for the multi-centre atomCAT2 study, which aims to develop and validate robust prognostic models for three clinically important outcomes in anal cancer following chemoradiotherapy. Methods This is a retrospective multi-centre cohort study, investigating overall survival, locoregional control and freedom from distant metastasis after primary chemoradiotherapy for anal squamous cell carcinoma. Patient data will be extracted and organised at each participating radiotherapy centre (n = 18). Candidate prognostic factors have been identified through literature review and expert opinion. Summary statistics will be calculated and exchanged between centres prior to modelling. The primary analysis will involve developing and validating Cox proportional hazards models across centres for each outcome through distributed learning. Outcomes at specific timepoints of interest and factor effect estimates will be reported, allowing for outcome prediction for future patients. Discussion The atomCAT2 study will analyse one of the largest available cross-institutional cohorts of patients with anal cancer treated with chemoradiotherapy. The analysis aims to provide information on current international clinical practice outcomes and may aid the personalisation and design of future anal cancer clinical trials through contributing to a better understanding of patient risk stratification.

Introduction The incidence of renal tumours is increasing and anatomic imaging cannot reliably distinguish benign tumours from renal cell carcinoma. Up to 30% of renal tumours are benign, with oncocytomas the most common type. Biopsy has not been routinely adopted in many centres due to concerns surrounding non-diagnostic rate, bleeding and tumour seeding. As a result, benign masses are often unnecessarily surgically resected. 99mTc-sestamibi SPECT/CT has shown high diagnostic accuracy for benign renal oncocytomas and other oncocytic renal neoplasms of low malignant potential in single-centre studies. The primary aim of MULTI-MIBI is to assess feasibility of a multicentre study of 99mTc-sestamibi SPECT/CT against a reference standard of histopathology from surgical resection or biopsy. Secondary aims of the study include obtaining estimates of 99mTc-sestamibi SPECT/CT sensitivity and specificity and to inform the design and conduct of a future definitive trial.Methods and analysis A feasibility prospective multicentre study of participants with indeterminate, clinical T1 renal tumours to undergo 99mTc-sestamibi SPECT/CT (index test) compared with histopathology from biopsy or surgical resection (reference test). Interpretation of the index and reference tests will be blinded to the results of the other. Recruitment rate as well as estimates of sensitivity, specificity, positive and negative predictive value will be reported. Semistructured interviews with patients and clinicians will provide qualitative data to inform onward trial design and delivery. Training materials for 99mTc-sestamibi SPECT/CT interpretation will be developed, assessed and optimised. Early health economic modelling using a decision analytic approach for different diagnostic strategies will be performed to understand the potential cost-effectiveness of 99mTc-sestamibi SPECT/CT.Ethics and dissemination Ethical approval has been granted (UK HRA REC 20/YH/0279) protocol V.5.0 dated 21/6/2022. Study outputs will be presented and published nationally and internationally.Trial registration number ISRCTN12572202.

Introduction Radiotherapy is the most common curative treatment for non-metastatic prostate cancer; however, up to 13% of patients will develop local recurrence within 10 years. Patients can undergo further and potentially curative treatment including salvage surgery, brachytherapy (BT), external beam radiotherapy, high-intensity focused ultrasound and cryotherapy. Systematic review shows that high-dose-rate (HDR) BT and stereotactic body radiotherapy (SBRT) have the best outcomes in terms of biochemical control and lowest side effects. The reirradiation options for previously irradiated prostate cancer (RO-PIP) trial aims to determine the feasibility of recruitment to a trial randomising patients to salvage HDR-BT or SBRT and provide prospective data on patient recorded toxicity outcomes that will inform a future phase III trial.Methods and analysis The primary endpoint of the RO-PIP feasibility study is to evaluate the patient recruitment potential over 2 years to a trial randomising to either SBRT or HDR-BT for patients who develop local recurrence of prostate cancer following previous radiation therapy. The aim is to recruit 60 patients across 3 sites over 2 years and randomise 1:1 to SBRT or HDR-BT. Secondary objectives include recording clinician and patient-reported outcome measures to evaluate treatment-related toxicity. In addition, the study aims to identify potential imaging, genomic and proteomic biomarkers that are predictive of toxicity and outcome based on hypoxia status, a prognostic marker of prostate cancer.Ethics and dissemination This study has been approved by the Yorkshire and The Humber—Bradford Leeds Research Ethics Committee (Reference: 21/YH/0305, IRAS: 297060, January 2022). The results will be presented in national and international conferences, published in peer-reviewed journals and will be communicated to relevant stakeholders. A plain English report will be shared with the study participants, patients’ organisations and media.Trial registration number ISRCTN 12238218 (Amy Ackroyd NIHR CPMS Team).

Background: The study purpose was to develop a machine learning (ML)-based predictive model for event-free survival (EFS) in patients with hepatocellular carcinoma (HCC) undergoing stereotactic ablative radiotherapy (SABR). Methods: Patients receiving SABR for HCC at a single institution, between 2017 and 2020, were included in the study. They were split into training and test (85%:15%) cohorts. Events of interest were HCC recurrence or death. Three ML models were trained, the features were selected, and the hyperparameters were tuned. The performance was measured using Harrell's C index with the best-performing model being tested on the unseen cohort. Results: Overall, 41 patients were included (training = 34, test = 7) and 64 lesions were analysed (training = 50, test = 14), resulting in 30 events (60% rate) in the training set (death = 6, recurrence = 24) and 8 events (57% rate) in the test set (death = 5, recurrence = 3). A Cox regression model, using age at treatment, albumin, and intra-lesional fat identified through MRI as variables, had the best performance with a mean training score of 0.78 (standard deviation (SD) 0.02), a mean validation of 0.78 (SD 0.18), and a test score of 0.94. Conclusions: Predicting the outcomes in patients with HCC, following SABR, using a novel model is feasible and warrants further evaluation. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis in cardiovascular disease has largely focused on CT and MRI modalities. This scoping review aims to summarise the existing literature on radiomic analysis techniques in cardiovascular disease. Methods: MEDLINE and Embase databases were searched for eligible studies evaluating radiomic techniques in living human subjects derived from CT, MRI or PET imaging investigating atherosclerotic disease. Data on study population, imaging characteristics and radiomics methodology were extracted. Results: Twenty-nine studies consisting of 5753 patients (3752 males) were identified, and 78.7% of patients were from coronary artery studies. Twenty-seven studies employed CT imaging (19 CT carotid angiography and 6 CT coronary angiography (CTCA)), and two studies studied PET/CT. Manual segmentation was most frequently undertaken. Processing techniques included voxel discretisation, voxel resampling and filtration. Various shape, first-order, second-order and higher-order radiomic features were extracted. Logistic regression was most commonly used for machine learning. Conclusion: Most published evidence was feasibility/proof of concept work. There was significant heterogeneity in image acquisition, segmentation techniques, processing and analysis between studies. There is a need for the implementation of standardised imaging acquisition protocols, adherence to published reporting guidelines and economic evaluation. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A—RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C—Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.

Museum specimens provide a record of past species distribution and are an increasingly important resource for conservation genetic research. The scientific value of these specimens depends upon the veracity of their associated data and can be compromised by inaccurate details; including taxonomic identity, collection locality, and collector. New Zealand contains many endemic species that have been driven to extinction or reduced to relict distributions following the arrival of humans and mammalian predators, including the Acanthisittid wrens (of which only two of the eight described species presently persist). One of these is the New Zealand rock wren (Xenicus gilviventris), currently classified as an endangered species and experiencing ongoing population declines. Here we analyze ancient DNA retrieved from New Zealand rock wren museum skins to establish the veracity of their recorded collection localities—New Zealand rock wrens exhibit strong north-south genetic structuring along the Southern Alps of New Zealand's South Island. We include the only specimen reportedly collected from New Zealand's North Island, outside the known range of New Zealand rock wrens, specimens collected by Henry Hamersley Travers, a collector known for poor record keeping and potentially fraudulent specimen data, and type specimens of proposed Xenicus taxa. Multiple instances of inaccurate collection locality were detected, including that of the New Zealand rock wren reportedly collected from the North Island, which matches individuals from the southern South Island. Syntypes of X. haasti, and a syntype of X. gilviventris clustered with individuals belonging to the northern New Zealand rock wren lineage. Our results suggest that New Zealand rock wrens have not been historically extirpated from New Zealand's North Island, and that caution must be taken when utilizing museum specimens to inform conservation management decisions. Additionally, we describe the type locality of both X. gilviventris and X. haasti, with genetic and historical evidence suggesting that the specimens used to describe these taxa were collected from the headwaters of the Rakaia River. This study demonstrates that ancient DNA analysis can add value to museum specimens by revealing incorrect specimen data and inform the conservation management and taxonomy of endangered species.

Simple Summary: This study investigates the effectiveness of three imaging methods—T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography-computed tomography (2-[18F]FDG PET-CT)—individually and combined, in assessing treatment response for locally advanced cervical carcinoma (LACC). As the third most common cancer worldwide, precise post-treatment evaluation is crucial for planning and follow-up. This research addresses the lack of a standardised response assessment after chemoradiotherapy for LACC, introducing a five-point qualitative scale for assessment. The findings aim to fill knowledge gaps in treatment response evaluation, potentially influencing clinical practices for better patient outcomes in cervical cancer management. Purpose: To evaluate the utility and comparative effectiveness of three five-point qualitative scoring systems for assessing response on PET-CT and MRI imaging individually and in combination, following curative-intent chemoradiotherapy (CRT) in locally advanced cervical cancer (LACC). Their performance in the prediction of subsequent patient outcomes was also assessed; Methods: Ninety-seven patients with histologically confirmed LACC treated with CRT using standard institutional protocols at a single centre who underwent PET-CT and MRI at staging and post treatment were identified retrospectively from an institutional database. The post-CRT imaging studies were independently reviewed, and response assessed using five-point scoring tools for T2WI, DWI, and FDG PET-CT. Patient characteristics, staging, treatment, and follow-up details including progression-free survival (PFS) and overall survival (OS) outcomes were collected. To compare diagnostic performance metrics, a two-proportion z-test was employed. A Kaplan–Meier analysis (Mantel–Cox log-rank) was performed. Results: The T2WI (p < 0.00001, p < 0.00001) and DWI response scores (p < 0.00001, p = 0.0002) had higher specificity and accuracy than the PET-CT. The T2WI score had the highest positive predictive value (PPV), while the negative predictive value (NPV) was consistent across modalities. The combined MR scores maintained high NPV, PPV, specificity, and sensitivity, and the PET/MR consensus scores showed superior diagnostic accuracy and specificity compared to the PET-CT score alone (p = 0.02926, p = 0.0083). The Kaplan–Meier analysis revealed significant differences in the PFS based on the T2WI (p < 0.001), DWI (p < 0.001), combined MR (p = 0.003), and PET-CT/MR consensus scores (p < 0.001) and in the OS for the T2WI (p < 0.001), DWI (p < 0.001), and combined MR scores (p = 0.031) between responders and non-responders. Conclusion: Post-CRT response assessment using qualitative MR scoring and/or consensus PET-CT and MRI scoring was a better predictor of outcome compared to PET-CT assessment alone. This requires validation in a larger prospective study but offers the potential to help stratify patient follow-up in the future. [ABSTRACT FROM AUTHOR]