Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a representation of tissue regions as well as a metric of the clustering to obtain their underlying patterns. These histopathological patterns are then used to represent the interaction between complex tissues and predict clinical outcomes directly. We furthermore show that the proposed approach can benefit from linear predictors to avoid overfitting in patient outcomes predictions. To this end, we introduce a new well-characterized clinicopathological dataset, including a retrospective collective of 374 patients, with their survival time and treatment information. Histomorphological clusters obtained by our method are evaluated by training survival models. The experimental results demonstrate statistically significant patient stratification, and our approach outperformed the state-of-the-art deep clustering methods

Protocol for Physiotherapy OR Tvt Randomised Efficacy Trial (PORTRET): a multicentre randomised controlled trial to assess the cost-effectiveness of the tension free vaginal tape versus pelvic floor muscle training in women with symptomatic moderate to severe stress urinary incontinence

<p>Abstract</p> <p>Background</p> <p>Stress urinary incontinence is a common condition affecting approximately 20% of adult women causing substantial individual (quality of life) and economic (119 million Euro/year spent on incontinence pads in the Netherlands) burden. Pelvic floor muscle training (PFMT) is regarded as first line treatment, but only 15-25% of women will be completely cured. Approximately 65% will report that their condition improved, but long term adherence to treatment is problematic. In addition, at longer term (2-15 years) follow-up 30-50% of patients will end up having surgery. From 1996 a minimal invasive surgical procedure, the Tension-free Vaginal Tape (TVT) has rapidly become the gold standard in surgical treatment of stress urinary incontinence. With TVT 65-95% of women are cured. However, approximately 3-6% of women will develop symptoms of an overactive bladder, resulting in reduced quality of life. Because of its efficacy the TVT appears to be preferable over PFMT but both treatments and their costs have not been compared head-to-head in a randomised clinical trial.</p> <p>Methods/Design</p> <p>A multi-centre randomised controlled trial will be performed for women between 35 - 80 years old with moderate to severe, predominantly stress, urinary incontinence, who have not received specialised PFMT or previous anti-incontinence surgery. Women will be assigned to either PFMT by a specialised physiotherapist for a standard of 9-18 session in a period of 6 months, or TVT(O) surgery. The main endpoint of the study is the subjective improvement of urinary incontinence. As secondary outcome the objective cure will be assessed from history and clinical parameters. Subjective improvement in quality of life will be measured by generic (EQ-5D) and disease-specific (Urinary Distress Inventory and Incontinence Impact Questionnaire) quality of life instruments. The economical endpoint is short term (1 year) incremental cost-effectiveness in terms of costs per additional year free of urinary incontinence and costs per Quality Adjusted Life Years (QALY) gained. Finally, treatment strategy and patient characteristics will be combined in a prediction model, to allow for individual treatment decisions in future patients. Four hundred female patients will be recruited from over 30 hospitals in the Netherlands</p> <p>Trial registration</p> <p>Nederlands trial register: NTR 1248</p

Cubic-spline interpolation to estimate effects of inbreeding on milk yield in first lactation Holstein cows

Milk yield records (305d, 2X, actual milk yield) of 123,639 registered first lactation Holstein cows were used to compare linear regression (y = β0 + β1X + e), quadratic regression, (y = β0 + β1X + β2X2 + e) cubic regression (y = β0 + β1X + β2X2 + β3X3 +e) and fixed factor models, with cubic-spline interpolation models, for estimating the effects of inbreeding on milk yield. Ten animal models, all with herd-year-season of calving as fixed effect, were compared using the Akaike corrected-Information Criterion (AICc). The cubic-spline interpolation model with seven knots had the lowest AICc, whereas for all those labeled as “traditional”, AICc was higher than the best model. Results from fitting inbreeding using a cubic-spline with seven knots were compared to results from fitting inbreeding as a linear covariate or as a fixed factor with seven levels. Estimates of inbreeding effects were not significantly different between the cubic-spline model and the fixed factor model, but were significantly different from the linear regression model. Milk yield decreased significantly at inbreeding levels greater than 9%. Variance component estimates were similar for the three models. Ranking of the top 100 sires with daughter records remained unaffected by the model used

Survival of patients with nonseminomatous germ cell cancer: a review of the IGCC classification by Cox regression and recursive partitioning

The International Germ Cell Consensus (IGCC) classification identifies good, intermediate and poor prognosis groups among patients with metastatic nonseminomatous germ cell tumours (NSGCT). It uses the risk factors primary site, presence of nonpulmonary visceral metastases and tumour markers alpha-fetoprotein (AFP), human chorionic gonadotrophin (HCG) and lactic dehydrogenase (LDH). The IGCC classification is easy to use and remember, but lacks flexibility. We aimed to examine the extent of any loss in discrimination within the IGCC classification in comparison with alternative modelling by formal weighing of the risk factors. We analysed survival of 3048 NSGCT patients with Cox regression and recursive partitioning for alternative classifications. Good, intermediate and poor prognosis groups were based on predicted 5-year survival. Classifications were further refined by subgrouping within the poor prognosis group. Performance was measured primarily by a bootstrap corrected c-statistic to indicate discriminative ability for future patients. The weights of the risk factors in the alternative classifications differed slightly from the implicit weights in the IGCC classification. Discriminative ability, however, did not increase clearly (IGCC classification, c=0.732; Cox classification, c=0.730; Recursive partitioning classification, c=0.709). Three subgroups could be identified within the poor prognosis groups, resulting in classifications with five prognostic groups and slightly better discriminative ability (c = 0.740). In conclusion, the IGCC classification in three prognostic groups is largely supported by Cox regression and recursive partitioning. Cox regression was the most promising tool to define a more refined classification

Can the Tumor Deposits Be Counted as Metastatic Lymph Nodes in the UICC TNM Staging System for Colorectal Cancer?

OBJECTIVE: The 7th edition of AJCC staging manual implicitly states that only T1 and T2 lesions that lack regional lymph node metastasis but have tumor deposit(s) will be classified in addition as N1c, though it is not consistent in that pN1c is also an option for pT3/T4a tumors in the staging table. Nevertheless, in this TNM classification, how to classify tumor deposits (TDs) in colorectal cancer patients with lymph node metastasis (LNM) and TDs simultaneously is still not clear. The aim of this study is to investigate the possibility of counting TDs as metastatic lymph nodes in TNM classification and to identify its prognostic value for colorectal cancer patients. METHODS AND RESULTS: In this retrospective study, 513 cases of colorectal cancer with LNM were reviewed. We proposed a novel pN (npN) category in which TDs were counted as metastatic lymph nodes in the TNM classification. Cancer-specific survival according to the npN or pN category was analyzed using Kaplan-Meier survival curves. Univariate and multivariate analyses were performed to identify significant prognostic factors. Harrell's C statistic was used to test the predictive capacity of the prognostic models. The results revealed that the TD was a significant prognostic factor in colorectal cancer. Univariate and multivariate analyses uniformly indicated that the npN category was significantly correlated with prognosis. The results of Harrell's C statistical analysis demonstrated that the npN category exhibited a superior predictive capacity compared to the pN category of the 7th edition TNM classification. Moreover, we also found no significant prognostic differences in patients with or without TD in the same npN categories. CONCLUSIONS: The counting of TDs as metastatic lymph nodes in the TNM classification system is potentially superior to the classification in the 7th edition of the TNM staging system to assess prognosis and survival for colorectal cancer patients

The melanoma-specific graded prognostic assessment does not adequately discriminate prognosis in a modern population with brain metastases from malignant melanoma

The melanoma-specific graded prognostic assessment (msGPA) assigns patients with brain metastases from malignant melanoma to 1 of 4 prognostic groups. It was largely derived using clinical data from patients treated in the era that preceded the development of newer therapies such as BRAF, MEK and immune checkpoint inhibitors. Therefore, its current relevance to patients diagnosed with brain metastases from malignant melanoma is unclear. This study is an external validation of the msGPA in two temporally distinct British populations.Performance of the msGPA was assessed in Cohort I (1997-2008, n=231) and Cohort II (2008-2013, n=162) using Kaplan-Meier methods and Harrell's c-index of concordance. Cox regression was used to explore additional factors that may have prognostic relevance.The msGPA does not perform well as a prognostic score outside of the derivation cohort, with suboptimal statistical calibration and discrimination, particularly in those patients with an intermediate prognosis. Extra-cerebral metastases, leptomeningeal disease, age and potential use of novel targeted agents after brain metastases are diagnosed, should be incorporated into future prognostic models.An improved prognostic score is required to underpin high-quality randomised controlled trials in an area with a wide disparity in clinical care

Predicting hospital stay, mortality and readmission in people admitted for hypoglycaemia: prognostic models derivation and validation

Aims/hypothesis: Hospital admissions for hypoglycaemia represent a significant burden on individuals with diabetes and have a substantial economic impact on healthcare systems. To date, no prognostic models have been developed to predict outcomes following admission for hypoglycaemia. We aimed to develop and validate prediction models to estimate risk of inpatient death, 24 h discharge and one month readmission in people admitted to hospital for hypoglycaemia. Methods: We used the Hospital Episode Statistics database, which includes data on all hospital admission to National Health Service hospital trusts in England, to extract admissions for hypoglycaemia between 2010 and 2014. We developed, internally and temporally validated, and compared two prognostic risk models for each outcome. The first model included age, sex, ethnicity, region, social deprivation and Charlson score (‘base’ model). In the second model, we added to the ‘base’ model the 20 most common medical conditions and applied a stepwise backward selection of variables (‘disease’ model). We used C-index and calibration plots to assess model performance and developed a calculator to estimate probabilities of outcomes according to individual characteristics. Results: In derivation samples, 296 out of 11,136 admissions resulted in inpatient death, 1789/33,825 in one month readmission and 8396/33,803 in 24 h discharge. Corresponding values for validation samples were: 296/10,976, 1207/22,112 and 5363/22,107. The two models had similar discrimination. In derivation samples, C-indices for the base and disease models, respectively, were: 0.77 (95% CI 0.75, 0.80) and 0.78 (0.75, 0.80) for death, 0.57 (0.56, 0.59) and 0.57 (0.56, 0.58) for one month readmission, and 0.68 (0.67, 0.69) and 0.69 (0.68, 0.69) for 24 h discharge. Corresponding values in validation samples were: 0.74 (0.71, 0.76) and 0.74 (0.72, 0.77), 0.55 (0.54, 0.57) and 0.55 (0.53, 0.56), and 0.66 (0.65, 0.67) and 0.67 (0.66, 0.68). In both derivation and validation samples, calibration plots showed good agreement for the three outcomes. We developed a calculator of probabilities for inpatient death and 24 h discharge given the low performance of one month readmission models. Conclusions/interpretation: This simple and pragmatic tool to predict in-hospital death and 24 h discharge has the potential to reduce mortality and improve discharge in people admitted for hypoglycaemia