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Claudin-low breast cancer is a molecular type of breast cancer originally identified by gene expression profiling and reportedly associated with poor survival. Claudin-low tumors have been recognised to preferentially display a triple-negative phenotype, however only a minority of triple-negative breast cancers are claudin-low. We sought to identify an immunohistochemical profile for claudin-low tumors that could facilitate their identification in formalin fixed paraffin embedded tumor material. First, an in silico collection of ~1600 human breast cancer expression profiles was assembled and all claudin-low tumors identified. Second, genes differentially expressed between claudin-low tumors and all other molecular subtypes of breast cancer were identified. Third, a number of these top differentially expressed genes were tested using immunohistochemistry for expression in a diverse panel of breast cancer cell lines to determine their specificity for claudin-low tumors. Finally, the immunohistochemical panel found to be most characteristic of claudin-low tumors was examined in a cohort of 942 formalin fixed paraffin embedded human breast cancers with >10 years clinical follow-up to evaluate the clinico-pathologic and survival characteristics of this tumor subtype. Using this approach we determined that claudin-low breast cancer is typically negative for ER, PR, HER2, claudin 3, claudin 4, claudin 7 and E-cadherin. Claudin-low tumors identified with this immunohistochemical panel, were associated with young age of onset, higher tumor grade, larger tumor size, extensive lymphocytic infiltrate and a circumscribed tumor margin. Patients with claudin-low tumors had a worse overall survival when compared to patients with luminal A type breast cancer. Interestingly, claudin-low tumors were associated with a low local recurrence rate following breast conserving therapy. In conclusion, a limited panel of antibodies can facilitate the identification of claudin-low tumors. Furthermore, claudin-low tumors identified in this manner display similar clinical, pathologic and survival characteristics to claudin-low tumors identified from fresh frozen tumor material using gene expression profiling.

There is limited data on the care and outcomes of individuals with both chronic obstructive pulmonary disease (COPD) and lung cancer, particularly in advanced disease. We hypothesized such patients would receive less cancer treatment and have worse outcomes. We analyzed administrative data from the province of Ontario including demographics, hospitalization records, physician billings, cancer diagnosis, and treatments. COPD was defined using the ICES-derived COPD cohort (1996-2014) with data from 2002 to 2014. Descriptive statistics and multivariable analyses were undertaken. Of 105 304 individuals with lung cancer, 43 375 (41%) had stage data and 36 738 (34.9%) had COPD. Those with COPD were likely to be younger, have a Charlson score ≤ 1, have lower income, to live rurally, and to have stage I/II lung cancer (29.8 vs 26.5%; all p<0.001). For the COPD population with stage I/II cancer, surgery and adjuvant chemotherapy were less likely (56.8 vs. 65.9% and 15.4 vs. 17.1%, respectively), while radiation was more likely (26.0 vs. 21.8%) (p all < 0.001). In the stage III/IV population, individuals with COPD received less chemotherapy (55.9 vs 64.4%) or radiation (42.5 vs 47.5%; all p<0.001). Inhaler and oxygen use was higher those with COPD, as were hospitalizations for respiratory infections and COPD exacerbations. On multivariable analysis, overall survival was worse among those with COPD (HR 1.20, 95% CI 1.19-1.22). A co-diagnosis of COPD and lung cancer is associated with less curative treatment in early stage disease, less palliative treatment in late stage disease, and poorer outcomes.

Common Sense Oncology (CSO) prioritises treatments providing meaningful benefits for people with cancer. Here, we describe CSO principles aimed at improving the design, analysis, and reporting of randomised, controlled, phase 3 clinical trials evaluating cancer treatments. These principles include: (1) control treatment should be the best current standard of care; (2) the preferred primary endpoint is overall survival or a validated surrogate; (3) an absolute measure of benefit should be provided, such as the difference between groups in median overall survival times or the proportion of surviving patients at a prespecified time; (4) health-related quality of life should be at least a secondary endpoint; (5) toxicity should be described objectively without subjective language diminishing its importance; (6) trials should be designed to show or rule out clinically meaningful differences in outcomes, rather than a statistically significant difference alone; (7) censoring should be detailed, and sensitivity analyses done to determine its possible effects; (8) experimental treatments known to improve overall survival at later disease stages should be offered and funded for patients progressing in the control group; and (9) reports of trials should include a lay-language summary. We include checklists to guide compliance with these principles. By encouraging adherence, CSO aims to ensure that clinical trials yield results that are scientifically robust and meaningful to patients.

The goals of treatment for people with advanced cancer are to prolong survival and improve symptoms and health-related quality of life (HRQOL). Although many phase 3 randomised clinical trials seek to evaluate HRQOL during treatment, informing individual patients about expected HRQOL outcomes is challenging, as the common method of analysis and reporting compares averages for randomised groups, and clinicians find these data difficult to apply in clinical practice. Symptomatic patients with advanced cancer would like to know the probability that a proposed treatment might improve their survival or their dominant symptoms, and the probability of having treatment-related side-effects. When specifying HRQOL endpoints, we recommend that trialists develop HRQOL hypotheses about which dominant symptoms might be improved due to the initiation of the investigational treatment, and whether aspects of functioning and overall HRQOL will also improve despite the side-effects of the treatment. Validated, disease-specific, patient-reported outcome measures should be used to assess the relevant HRQOL concepts. Changes in HRQOL should be reported as the proportion of patients who have a specified improvement (or deterioration) in these relevant HRQOL scales (ie, as a response criterion), and harmonised standards for such a response criterion are needed.

Transrectal ultrasound (TRUS) guided biopsy for prostate cancer is prone to random and systemic error and has been shown to have a negative predictive value of 70%. PRECISION and PRECISE are among the first randomised studies to evaluate the new MRI-targeted biopsy (MRI-TB) pathway with a non-paired design to detect clinically significant prostate cancer and avoid unnecessary treatment. The trials' results individually demonstrated non-inferiority of MRI-TB compared to TRUS biopsy. An individual patient data (IPD) meta-analysis was planned from the outset of the two trials in parallel and this IPD meta-analysis aims to further elucidate the utility of MRI-TB as the optimal diagnostic pathway for prostate cancer. This study is registered on PROSPERO (CRD42021249263). A search of Medline, Embase, Cochrane Central Register of Registered Trials (CENTRAL), Web of Science, and ClinicalTrials.gov was performed up until 4th February 2021. Only randomised controlled trials (PRECISE, PRECISION and other eligible trials) comparing the MRI-targeted biopsy pathway and traditional TRUS biopsy pathway will be included. The primary outcome of the review is the proportion of men diagnosed with clinically significant prostate cancer in each arm (Gleason ≥ 3+4 = 7). IPD and study-level data and characteristics will be sought from eligible studies. Analyses will be done primarily using an intention-to-treat approach, and a one-step IPD meta-analysis will be performed using generalised linear mixed models. A non-inferiority margin of 5 percentage points will be used. Heterogeneity will be quantified using the variance parameters from the mixed model. If there is sufficient data, we will investigate heterogeneity by exploring the effect of the different conducts of MRIs, learning curves of MRI reporting and MRI targeted biopsies. This systematic review is registered on PROSPERO (CRD42021249263).

Background Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution and is often used for imaging and time series data, but there are no evaluations on its potential benefits for tabular health data. Augmentation increases sample size and is seen as a form of regularization that increases the diversity of small datasets, leading them to perform better on unseen data. Objectives Evaluate data augmentation using generative models on tabular health data and assess the impact of diversity versus increasing the sample size. Methods Using 13 large health datasets, we performed a simulation to evaluate the impact of data augmentation on the prediction performance (as measured by the ROC-AUC, the area under the receiver operating characteristic curve) on binary classification gradient boosted decision tree models. Four different synthetic data generation models were evaluated. We also built a generalized linear mixed effect model to assess the variable importance for model performance improvements from augmentation. We illustrate the proposed method on seven small real datasets as an application. A comparison of augmentation with resampling (which is a proxy for a larger dataset with minimal impact on diversity) was performed. Results Augmentation improves prognostic performance for datasets that have higher cardinality categorical variables and lower baseline ROC-AUC. No specific generative model consistently outperformed the others. For the seven small application datasets, augmenting the existing data results in an increase in ROC-AUC between 4.31% (ROC-AUC from 0.71 to 0.75) and 43.23% (ROC-AUC from 0.51 to 0.73), with an average 15.55% relative improvement, demonstrating the nontrivial impact of augmentation on small datasets ( p  = 0.0078). Augmentation ROC-AUC was higher than resampling only ROC-AUC ( p  = 0.016). The diversity of augmented datasets was higher than the diversity of resampled datasets ( p  = 0.046). Conclusions This study demonstrates that data augmentation using generative models can have a marked benefit in terms of improved predictive performance for machine learning models on tabular health data, but only for datasets that meet baseline data complexity and predictive performance criteria. Our mixed effect model identified the most influential characteristics of the dataset and can help end-users have a more realistic expectation of the augmentation performance for a new dataset. Furthermore, augmentation performed better when having a smaller dataset, which is consistent with the argument that greater data diversity due to augmentation is beneficial. Clinical trial registration Not applicable.

Background Chronic obstructive pulmonary disease (COPD) and lung cancer are associated diseases. COPD is underdiagnosed and thus undertreated, but there is limited data on COPD diagnosis in the setting of lung cancer. We assessed the diagnosis of COPD with lung cancer in a large public healthcare system. Methods Anonymous administrative data was acquired from ICES, which links demographics, hospital records, physician billing, and cancer registry data in Ontario, Canada. Individuals age 35 or older with COPD were identified through a validated, ICES-derived cohort and spirometry use was derived from physician billings. Statistical comparisons were made using Wilcoxon rank sum, Cochran-Armitage, and chi-square tests. Results From 2002 to 2014, 756,786 individuals were diagnosed with COPD, with a 2014 prevalence of 9.3%. Of these, 51.9% never underwent spirometry. During the same period, 105,304 individuals were diagnosed with lung cancer, among whom COPD was previously diagnosed in 34.9%. Having COPD prior to lung cancer was associated with lower income, a rural dwelling, a lower Charlson morbidity score, and less frequent stage IV disease (48 vs 54%, p  < 0.001). Spirometry was more commonly undertaken in early stage disease (90.6% in stage I-II vs. 54.4% in stage III-IV). Conclusion Over a third of individuals with lung cancer had a prior diagnosis of COPD. Among individuals with advanced lung cancer, greater use of spirometry and diagnosis of COPD may help to mitigate respiratory symptoms.

Cancer poses a significant global health burden. With advances in screening and treatment, there are now a growing number of cancer survivors with complex needs, requiring the involvement of multiple health care providers. Previous studies have identified problems related to communication and care coordination between primary care providers (PCPs) and cancer specialists. This study aimed to examine whether a web- and text-based asynchronous system (eOncoNote) could facilitate communication between PCPs and cancer specialists (oncologists and oncology nurses) to improve patient-reported continuity of care among patients receiving treatment or posttreatment survivorship care. In this pragmatic randomized controlled trial, a total of 173 patients were randomly assigned to either the intervention group (eOncoNote plus usual methods of communication between PCPs and cancer specialists) or a control group (usual communication only), including 104 (60.1%) patients in the survivorship phase (breast and colorectal cancer) and 69 (39.9%) patients in the treatment phase (breast and prostate cancer). The primary outcome was patient-reported team and cross-boundary continuity (Nijmegen Continuity Questionnaire). Secondary outcome measures included the Generalized Anxiety Disorder Screener (GAD-7), Patient Health Questionnaire on Major Depression, and Picker Patient Experience Questionnaire. Patients completed the questionnaires at baseline and at 2 points following randomization. Patients in the treatment phase completed follow-up questionnaires at 1 month and at either 4 months (patients with prostate cancer) or 6 months following randomization (patients with breast cancer). Patients in the survivorship phase completed follow-up questionnaires at 6 months and at 12 months following randomization. The results did not show an intervention effect on the primary outcome of team and cross-boundary continuity of care or on the secondary outcomes of depression and patient experience with their health care. However, there was an intervention effect on anxiety. In the treatment phase, there was a statistically significant difference in the change score from baseline to the 1-month follow-up for GAD-7 (mean difference -2.3; P=.03). In the survivorship phase, there was a statistically significant difference in the change score for GAD-7 between baseline and the 6-month follow-up (mean difference -1.7; P=.03) and between baseline and the 12-month follow-up (mean difference -2.4; P=.004). PCPs' and cancer specialists' access to eOncoNote is not significantly associated with patient-reported continuity of care. However, PCPs' and cancer specialists' access to the eOncoNote intervention may be a factor in reducing patient anxiety. ClinicalTrials.gov NCT03333785; https://clinicaltrials.gov/ct2/show/NCT03333785.

While multiple retrospective and matched analyses support improved progression-free survival and OS with alloHCT [3, 4, 5–6], other studies have raised concerns about its benefit in older patients, citing limited OS gains and negative impacts on quality of life [7, 8–9]. After excluding pediatric cases, those without complete demographic data, and patients not treated within one year of diagnosis or likely misclassified, our final cohort included 5213 adults who received active AML therapy (Fig. Multivariate cox regression analysis identified age, comorbidity burden, rural residence, and lower income as significant predictors of reduced transplant utilization (Table 1). 1 0.38 0.23 0.63 0.0002 0.60 0.35 1.05 0.07 ≥2 0.46 0.33 0.64 <0.0001 0.75 0.52 1.09 0.13 Time Interval: 2017 to 2022 Variable Value Univariate (Unadjusted) Models Multivariate (Adjusted) Model Hazard Ratio Lower Upper P-Value Hazard Ratio Lower Upper P-Value Intensive chemo No REF . . .