Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the ...first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T
-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval CI 0.481-0.743) to 0.75 (95% CI 0.64-0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.
Objectives
To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients ...on active surveillance (AS).
Methods
The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5–16 years’ experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong’s test.
Results
The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (
p
= 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34–0.77).
Conclusions
PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients.
Key Points
•
The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches.
•
The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up.
• The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.
Ovarian cancer has a poor survival rate due to late diagnosis and improved methods are needed for its early detection. Our primary objective was to identify and incorporate additional biomarkers into ...longitudinal models to improve on the performance of CA125 as a first-line screening test for ovarian cancer.
This case-control study nested within UKCTOCS used 490 serial serum samples from 49 women later diagnosed with ovarian cancer and 31 control women who were cancer-free. Proteomics-based biomarker discovery was carried out using pooled samples and selected candidates, including those from the literature, assayed in all serial samples. Multimarker longitudinal models were derived and tested against CA125 for early detection of ovarian cancer.
The best performing models, incorporating CA125, HE4, CHI3L1, PEBP4 and/or AGR2, provided 85.7% sensitivity at 95.4% specificity up to 1 year before diagnosis, significantly improving on CA125 alone. For Type II cases (mostly high-grade serous), models achieved 95.5% sensitivity at 95.4% specificity. Predictive values were elevated earlier than CA125, showing the potential of models to improve lead time.
We have identified candidate biomarkers and tested longitudinal multimarker models that significantly improve on CA125 for early detection of ovarian cancer. These models now warrant independent validation.
An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of ...risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK.
Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity.
None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model.
PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.
Purpose To investigate the accuracy of surface-based ultrasound-derived PSA-density (US-PSAD) versus gold-standard MRI-PSAD as a risk-stratification tool. Methods Single-centre prospective study of ...patients undergoing MRI for suspected prostate cancer (PCa). Four combinations of US-volumes were calculated using transperineal (TP) and transabdominal (TA) views, with triplanar measurements to calculate volume and US-PSAD. Intra-class correlation coefficient (ICC) was used to compare US and MRI volumes. Categorical comparison of MRI-PSAD and US-PSAD was performed at PSAD cut-offs <0.15, 0.15–0.20, and >0.20 ng/mL2 to assess agreement with MRI-PSAD risk-stratification decisions. Results 64 men were investigated, mean age 69 years and PSA 7.0 ng/mL. 36/64 had biopsy-confirmed prostate cancer (18 Gleason 3+3, 18 Gleason ≥3+4). Mean MRI-derived gland volume was 60 mL, compared to 56 mL for TA-US, and 65 mL TP-US. ICC demonstrated good agreement for all US volumes with MRI, with highest agreement for transabdominal US, followed by combined TA/TP volumes. Risk-stratification decisions to biopsy showed concordant agreement between triplanar MRI-PSAD and ultrasound-PSAD in 86–91% and 92–95% at PSAD thresholds of >0.15 ng/mL2 and >0.12 ng/mL2, respectively. Decision to biopsy at threshold >0.12 ng/mL2, demonstrated sensitivity ranges of 81–100%, specificity 85–100%, PPV 86–100% and NPV 83–100%. Transabdominal US provided optimal sensitivity of 100% for this clinical decision, with specificity 85%, and transperineal US provided optimal specificity of 100%, with sensitivity 87%. Conclusion Transperineal-US and combined TA-TP US-derived PSA density values compare well with standard MRI-derived values and could be used to provide accurate PSAD at presentation and inform the need for further investigations.
Purpose
To investigate the accuracy of surface-based ultrasound-derived PSA-density (US-PSAD) versus gold-standard MRI-PSAD as a risk-stratification tool.
Methods
Single-centre prospective study of ...patients undergoing MRI for suspected prostate cancer (PCa). Four combinations of US-volumes were calculated using transperineal (TP) and transabdominal (TA) views, with triplanar measurements to calculate volume and US-PSAD. Intra-class correlation coefficient (ICC) was used to compare US and MRI volumes. Categorical comparison of MRI-PSAD and US-PSAD was performed at PSAD cut-offs <0.15, 0.15–0.20, and >0.20 ng/mL
2
to assess agreement with MRI-PSAD risk-stratification decisions.
Results
64 men were investigated, mean age 69 years and PSA 7.0 ng/mL. 36/64 had biopsy-confirmed prostate cancer (18 Gleason 3+3, 18 Gleason ≥3+4). Mean MRI-derived gland volume was 60 mL, compared to 56 mL for TA-US, and 65 mL TP-US. ICC demonstrated good agreement for all US volumes with MRI, with highest agreement for transabdominal US, followed by combined TA/TP volumes. Risk-stratification decisions to biopsy showed concordant agreement between triplanar MRI-PSAD and ultrasound-PSAD in 86–91% and 92–95% at PSAD thresholds of >0.15 ng/mL
2
and >0.12 ng/mL
2
, respectively. Decision to biopsy at threshold >0.12 ng/mL
2
, demonstrated sensitivity ranges of 81–100%, specificity 85–100%, PPV 86–100% and NPV 83–100%. Transabdominal US provided optimal sensitivity of 100% for this clinical decision, with specificity 85%, and transperineal US provided optimal specificity of 100%, with sensitivity 87%.
Conclusion
Transperineal-US and combined TA-TP US-derived PSA density values compare well with standard MRI-derived values and could be used to provide accurate PSAD at presentation and inform the need for further investigations.
There remains disagreement on the long-term effect of mammographic screening in women aged 40-49 years.
The long-term follow-up of a randomised controlled trial that offered annual mammography to ...women aged 40-49 years. The estimation of the effect of these mammograms on breast cancer and other-cause mortality, and the effect on incidence, with implications for overdiagnosis.
An individually randomised controlled trial comparing offering annual mammography with offering usual care in those aged 40-48 years, and thus evaluating the effect of annual screening entirely taking place before the age of 50 years. There was follow-up for an average of 23 years for breast cancer incidence, breast cancer death and death from other causes. We analysed the mortality and incidence data by Poisson regression and estimated overdiagnosis formally using Markov process models.
Twenty-three screening units in England, Wales and Scotland within the NHS Breast Screening Programme.
Women aged 39-41 years were recruited between 1990 and 1997. After exclusions, a total of 53,883 women were randomised to undergo screening (the intervention group) and 106,953 women were randomised to have usual care (the control group).
The intervention group was invited to an annual breast screen with film mammography, two view at first screen and single view thereafter, up to and including the calendar year of their 48th birthday. The control group received no intervention. Both groups were invited to the National Programme from the age of 50 years, when screening is offered to all women in the UK.
The main outcome measures were mortality from breast cancers diagnosed during the intervention phase of the trial (i.e. before the first National Programme screen at 50 years), mortality from all breast cancers diagnosed after randomisation, all-cause mortality, mortality from causes other than breast cancer, and the incidence of breast cancer.
There was a statistically significant 25% reduction in mortality from breast cancers diagnosed during the intervention phase at 10 years' follow-up (relative rate 0.75, 95% confidence interval 0.58 to 0.97;
= 0.03). No reduction was observed thereafter (relative rate 0.98, 95% confidence interval 0.79 to 1.22). Overall, there was a statistically non-significant 12% reduction (relative rate 0.88, 95% confidence interval 0.74 to 1.03;
= 0.1). The absolute benefit remained approximately constant over time, at one death prevented per 1000 women screened. There was no effect of intervention on other-cause mortality (relative rate 1.02, 95% confidence interval 0.97 to 1.07;
= 0.4). The intervention group had a higher incidence of breast cancer than the control group during the intervention phase of the trial, but incidence equalised immediately on the first National Programme screen at the age of 50-52 years.
There was 31% average non-compliance with screening and three centres had to cease screening for resource and capacity reasons.
Annual mammographic screening at the age of 40-49 years resulted in a relative reduction in mortality, which was attenuated after 10 years. It is likely that digital mammography with two views at all screens, as practised now, could improve this further. There was no evidence of overdiagnosis in addition to that which already results from the National Programme carried out at later ages.
There is a need for research on the effects of modern mammographic protocols and additional imaging in this age group.
Current Controlled Trials ISRCTN24647151.
This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in
; Vol. 24, No. 55. See the NIHR Journals Library website for further project information. Other funding in the past has been received from the Medical Research Council, Cancer Research UK, the Department of Health and Social Care, the US National Cancer Institute and the American Cancer Society.
Background
The psychological effects from the COVID-19 pandemic and response are poorly understood.
Objective
To understand the effects of the pandemic and response on anxiety and health utility in a ...nationally representative sample of US adults.
Design
A de-identified, cross-sectional survey was administered at the end of April 2020. Probability weights were assigned using estimates from the 2018 American Community Survey and Integrated Public Use Microdata Series Estimates.
Participants
US adults 18–85 years of age with landline, texting-enabled cellphone, or internet access.
Intervention
Seven split-half survey blocks of 30 questions, assessing demographics, COVID-19-related health attitudes, and standardized measures of generalized self-efficacy, anxiety, depression, personality, and generic health utility.
Main Measures
State/Trait anxiety scores, EQ-5D-3L Visual Analog Scale (VAS) score, and demographic predictors of these scores.
Key Results
Among 4855 respondents, 56.7% checked COVID-19-related news several times daily, and 84.4% at least once daily. Only 65.7% desired SARS-CoV-2 vaccination for themselves, and 70.1% for their child. Mean state anxiety (S-anxiety) score was significantly higher than mean trait anxiety (T-anxiety) score (44.9, 95%CI 43.5–46.3 vs. 41.6, 95%CI 38.7–44.5;
p
= 0.03), with both scores significantly higher than previously published norms. In an adjusted regression model, less frequent news viewing was associated with significantly lower S-anxiety score. Mean EQ-5D-3L VAS score for the population was significantly lower vs. established US normative data (71.4 CI 67.4–75.5, std. error 2 vs. societal mean 80, std. error 0.1;
p
< 0.001). EQ-5D-3L VAS score was bimodal (highest with hourly and no viewing) and significantly reduced with less media viewership in an adjusted model.
Conclusions
Among a nationally representative sample, there were higher S-anxiety and lower EQ-5D-3L VAS scores compared to non-pandemic normative data, indicative of a potential detrimental acute effect of the pandemic. More frequent daily media viewership was significantly associated with higher S-anxiety but also predictive of higher health utility, as measured by EQ-5D-3L VAS scores.
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with around 9% of patients surviving >5 years. Asymptomatic in its initial stages, PDAC is mostly diagnosed late, when already ...a locally advanced or metastatic disease, as there are no useful biomarkers for detection in its early stages, when surgery can be curative. We have previously described a promising biomarker panel (LYVE1, REG1A, and TFF1) for earlier detection of PDAC in urine. Here, we aimed to establish the accuracy of an improved panel, including REG1B instead of REG1A, and an algorithm for data interpretation, the PancRISK score, in additional retrospectively collected urine specimens. We also assessed the complementarity of this panel with CA19-9 and explored the daily variation and stability of the biomarkers and their performance in common urinary tract cancers.
Clinical specimens were obtained from multiple centres: Barts Pancreas Tissue Bank, University College London, University of Liverpool, Spanish National Cancer Research Center, Cambridge University Hospital, and University of Belgrade. The biomarker panel was assayed on 590 urine specimens: 183 control samples, 208 benign hepatobiliary disease samples (of which 119 were chronic pancreatitis), and 199 PDAC samples (102 stage I-II and 97 stage III-IV); 50.7% were from female individuals. PDAC samples were collected from patients before treatment. The samples were assayed using commercially available ELISAs. Statistical analyses were performed using non-parametric Kruskal-Wallis tests adjusted for multiple comparisons, and multiple logistic regression. Training and validation datasets for controls and PDAC samples were obtained after random division of the whole available dataset in a 1:1 ratio. The substitution of REG1A with REG1B enhanced the performance of the panel to detect resectable PDAC. In a comparison of controls and PDAC stage I-II samples, the areas under the receiver operating characteristic curve (AUCs) increased from 0.900 (95% CI 0.843-0.957) and 0.926 (95% CI 0.843-1.000) in the training (50% of the dataset) and validation sets, respectively, to 0.936 in both the training (95% CI 0.903-0.969) and the validation (95% CI 0.888-0.984) datasets for the new panel including REG1B. This improved panel showed both sensitivity (SN) and specificity (SP) to be >85%. Plasma CA19-9 enhanced the performance of this panel in discriminating PDAC I-II patients from controls, with AUC = 0.992 (95% CI 0.983-1.000), SN = 0.963 (95% CI 0.913-1.000), and SP = 0.967 (95% CI 0.924-1.000). We demonstrate that the biomarkers do not show significant daily variation, and that they are stable for up to 5 days at room temperature. The main limitation of our study is the low number of stage I-IIA PDAC samples (n = 27) and lack of samples from individuals with hereditary predisposition to PDAC, for which specimens collected from control individuals were used as a proxy.
We have successfully validated our urinary biomarker panel, which was improved by substituting REG1A with REG1B. At a pre-selected cutoff of >80% SN and SP for the affiliated PancRISK score, we demonstrate a clinically applicable risk stratification tool with a binary output for risk of developing PDAC ('elevated' or 'normal'). PancRISK provides a step towards precision surveillance for PDAC patients, which we will test in a prospective clinical study, UroPanc.
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