Dangers of using "optimal" cutpoints in the evaluation of prognostic factors

Impact of preoperative haemoglobin, albumin, lymphocyte, and platelet score on oral cancer prognosis

OBJECTIVE

To evaluate the preoperative haemoglobin, albumin, lymphocyte, and platelet score as a prognostic indicator in oral squamous cell carcinoma treated by radical surgery.

SUBJECTS AND METHODS

Patients (83 men, 32 women; 65.80 ± 11.47 years) who underwent radical surgery between 2012 and 2022 were included. Factors affecting overall survival and disease-free survival according to the haemoglobin, albumin, lymphocyte, and platelet score were examined. Patients were categorised into low- and high-score groups using optimal cut-off values obtained from receiver operating characteristic curve analysis.

RESULTS

The low-score group had poorer overall and disease-free survival (p < 0.001 each). Multivariate analysis identified alcohol consumption (hazard ratio [HR], 3.83; 95% confidence interval [CI]: 1.56-9.41, p = 0.003); vascular invasion (HR, 3.97; 95% CI: 1.60-9.85, p = 0.003); and the haemoglobin, albumin, lymphocyte, and platelet score (HR, 0.39; 95% CI: 0.20-0.78, p = 0.007) as independent prognostic factors for overall survival and vascular (HR, 3.66; 95% CI: 1.79-7.50, p < 0.001) and lymphovascular (HR, 2.44; 95% CI: 1.36-4.41, p = 0.003) invasion as independent prognostic factors for disease-free survival.

CONCLUSION

The preoperative haemoglobin, albumin, lymphocyte, and platelet score may be a significant prognostic factor for patients with oral squamous cell carcinoma undergoing radical surgery.

Absence of association between low calf circumference, adjusted or not for adiposity, and ICU mortality in critically ill adults: A secondary analysis of a cohort study

BACKGROUND

Despite its correlation with skeletal muscle mass and its predictive value for adverse outcomes in clinical settings, calf circumference is a metric underexplored in intensive care. We aimed to determine whether adjusting low calf circumference for adiposity provides prognostic value superior to its unadjusted measurement for intensive care unit (ICU) mortality and other clinical outcomes in critically ill patients.

METHODS

In a secondary analysis of a cohort study across five ICUs, we assessed critically ill patients within 24 h of ICU admission. We adjusted calf circumference for body mass index (BMI) (25-29.9, 30-39.9, and ≥40) by subtracting 3, 7, or 12 cm from it, respectively. Values ≤34 cm for men and ≤33 cm for women identified low calf circumference.

RESULTS

We analyzed 325 patients. In the primary risk-adjusted analysis, the ICU death risk was similar between the low and preserved calf circumference (BMI-adjusted) groups (hazard ratio, 0.90; 95% CI, 0.47-1.73). Low calf circumference (unadjusted) increased the odds of ICU readmission 2.91 times (95% CI, 1.40-6.05). Every 1-cm increase in calf circumference as a continuous variable reduced ICU readmission odds by 12%. Calf circumference showed no significant association with other clinical outcomes.

CONCLUSION

BMI-adjusted calf circumference did not exhibit independent associations with ICU and in-hospital death, nor with ICU and in-hospital length of stay, compared with its unadjusted measurement. However, low calf circumference (unadjusted and BMI-adjusted) was independently associated with ICU readmission, mainly when analyzed as a continuous variable.

Txnrd1 as a prognosticator for recurrence, metastasis and response to neoadjuvant chemotherapy and radiotherapy in breast cancer patients

Thioredoxin reductase 1 (Txnrd1) is known to have prognostic significance in a subset of breast cancer patients. Despite the pivotal role of Txnrd1 in regulating several cellular and physiological processes in cancer progression and metastasis, its clinical significance is largely unrecognized. Here, we undertook a retrospective comprehensive meta-analysis of 13,322 breast cancer patients from 43 independent cohorts to assess prognostic and predictive roles of Txnrd1. We observed that Txnrd1 has a positive correlation with tumor grade and size and it is over-expressed in higher-grade and larger tumors. Further, hormone receptor-negative and HER2-positive tumors exhibit elevated Txnrd1 gene expression. Patients with elevated Txnrd1 expression exhibit significant hazards for shorter disease-specific and overall survival. While Txnrd1 has a positive correlation with tumor recurrence and metastasis, it has a negative correlation with time to recurrence and metastasis. Txnrd1High patients exhibit 2.5 years early recurrence and 1.3 years early metastasis as compared to Txnrd1Low cohort. Interestingly, patients with high Txnrd1 gene expression exhibit a pathologic complete response (pCR) to neoadjuvant chemotherapy, but they experience early recurrence after radiotherapy. Txnrd1High MDA-MB-231 cells exhibit significant ROS generation and reduced viability after doxorubicin treatment compared to Txnrd1Low MCF7 cells. Corroborating with findings from meta-analysis, Txnrd1 depletion leads to decreased survival, enhanced sensitivity to radiation induced killing, poor scratch-wound healing, and reduced invasion potential in MDA-MB-231 cells. Thus, Txnrd1 appears to be a potential predictor of recurrence, metastasis and therapy response in breast cancer patients.

Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions

BACKGROUND AND OBJECTIVE

Despite the constant improvement of coronary heart disease (CHD) diagnostics and treatment methods it remains one of the main causes of death in most countries around the world. And myocardial infarction with ST segment elevation on the electrocardiogram (STEMI) still is one of the most dangerous clinical variants of CHD. This study aims to develop an explainable machine learning model for in-hospital mortality (IHM) risk prediction in STEMI patients after myocardial revascularization by percutaneous coronary intervention (PCI).

METHODS

A single-center observational retrospective study was conducted, enrolling 4677 electronic medical records of patients with STEMI after PCI, which were analyzed using statistical analysis and machine learning methods. A pool of potential IHM predictors was identified, and prognostic models were developed and validated based on multivariate logistic regression, random forest, and stochastic gradient boosting methods at two stages of hospital treatment: during the initial physicians examination in the emergency department and immediately after PCI surgery. To explain the IHM prognosis, threshold values of IHM risk factors were determined using 3 grid search methods for optimal cut-off points, calculating centroids and SHapley Additive exPlanations (SHAP).

RESULTS

IHM prognostic models were developed using clinical and functional status data of STEMI patients during two stages of hospital treatment. The IHM prediction accuracy according to the first scenario was AUC = 0.85, and according to the second - AUC = 0.9. Predictors identified and validated in the models were converted into risk factors. Models whose parameters were risk factors demonstrated high forecast accuracy (AUC = 0.87), with the best model formed using the SHAP method.

CONCLUSIONS

For the forecast result interpretation risk factors obtained by categorizing continuous variables can be used by assessing the impact of the latter on the end point using the SHAP method.

CTPS1 is a novel therapeutic target in multiple myeloma which synergizes with inhibition of CHEK1, ATR or WEE1

Targeting nucleotide biosynthesis is a proven strategy for the treatment of cancer but is limited by toxicity, reflecting the fundamental nucleotide requirement of dividing cells. The rate limiting step in de novo pyrimidine synthesis is of interest, being catalyzed by two homologous enzymes, CTP synthase 1 (CTPS1) and CTPS2, that could be differentially targeted. Herein, analyses of publicly available datasets identified an essential role for CTPS1 in multiple myeloma (MM), linking high expression of CTPS1 (but not CTPS2) with advanced disease and poor outcomes. In cellular experiments, CTPS1 knockout induced apoptosis of MM cell lines. Exposure of MM cells to STP-B, a novel and highly selective pharmacological inhibitor of CTPS1, inhibited proliferation, induced S phase arrest and led to cell death by apoptosis. Mechanistically, CTPS1 inhibition by STP-B activated DNA damage response (DDR) pathways and induced double-strand DNA breaks which accumulated in early S phase. Combination of STP-B with pharmacological inhibitors of key components of the DDR pathway (ATR, CHEK1 or WEE1) resulted in synergistic growth inhibition and early apoptosis. Taken together, these findings identify CTPS1 as a promising new target in MM, either alone or in combination with DDR pathway inhibition.

Prognostic value of blood-based protein biomarkers in non-small cell lung cancer: A critical review and 2008-2022 update

BACKGROUND

Therapeutic possibilities for non-small cell lung cancer (NSCLC) have considerably increased during recent decades.

OBJECTIVE

To summarize the prognostic relevance of serum tumor markers (STM) for early and late-stage NSCLC patients treated with classical chemotherapies, novel targeted and immune therapies.

METHODS

A PubMed database search was conducted for prognostic studies on carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase, squamous-cell carcinoma antigen, progastrin-releasing-peptide, CA125, CA 19-9 and CA 15-3 STMs in NSCLC patients published from 2008 until June 2022.

RESULTS

Out of 1069 studies, 141 were identified as meeting the inclusion criteria. A considerable heterogeneity regarding design, patient number, analytical and statistical methods was observed. High pretherapeutic CYFRA 21-1 levels and insufficient decreases indicated unfavorable prognosis in many studies on NSCLC patients treated with chemo-, targeted and immunotherapies or their combinations in early and advanced stages. Similar results were seen for CEA in chemotherapy, however, high pretherapeutic levels were sometimes favorable in targeted therapies. CA125 is a promising prognostic marker in patients treated with immunotherapies. Combinations of STMs further increased the prognostic value over single markers.

CONCLUSION

Protein STMs, especially CYFRA 21-1, have prognostic potential in early and advanced stage NSCLC. For future STM investigations, better adherence to comparable study designs, analytical methods, outcome measures and statistical evaluation standards is recommended.

Integrative analysis of clinicopathological features defines novel prognostic models for mantle cell lymphoma in the immunochemotherapy era: a report from The North American Mantle Cell Lymphoma Consortium

BACKGROUND

Patients with mantle cell lymphoma (MCL) exhibit a wide variation in clinical presentation and outcome. However, the commonly used prognostic models are outdated and inadequate to address the needs of the current multidisciplinary management of this disease. This study aims to investigate the clinical and pathological features of MCL in the immunochemotherapy era and improve the prognostic models for a more accurate prediction of patient outcomes.

METHODS

The North American Mantle Cell Lymphoma Project is a multi-institutional collaboration of 23 institutions across North America to evaluate and refine prognosticators for front-line therapy. A total of 586 MCL cases diagnosed between 2000 and 2012 are included in this study. A comprehensive retrospective analysis was performed on the clinicopathological features, treatment approaches, and outcomes of these cases. The establishment of novel prognostic models was based on in-depth examination of baseline parameters, and subsequent validation in an independent cohort of MCL cases.

RESULTS

In front-line strategies, the use of hematopoietic stem cell transplantation was the most significant parameter affecting outcomes, for both overall survival (OS, p < 0.0001) and progression-free survival (PFS, p < 0.0001). P53 positive expression was the most significant pathological parameter correlating with inferior outcomes (p < 0.0001 for OS and p = 0.0021 for PFS). Based on the baseline risk factor profile, we developed a set of prognostic models incorporating clinical, laboratory, and pathological parameters that are specifically tailored for various applications. These models, when tested in the validation cohort, exhibited strong predictive power for survival and showed a stratification resembling the training cohort.

CONCLUSIONS

The outcome of patients with MCL has markedly improved over the past two decades, and further enhancement is anticipated with the evolution of clinical management. The innovative prognostic models developed in this study would serve as a valuable tool to guide the selection of more suitable treatment strategies for patients with MCL.

Thoracic skeletal muscle index is effective for CT-defined sarcopenia evaluation in patients with head and neck cancer

PURPOSE

Computed tomography (CT)-defined sarcopenia, as a measurement of low skeletal muscle (SM), is a poor prognostic indicator in patients with head and neck cancer (HNC), independent of weight or nutritional status. We used SM measures at the second thoracic vertebra (T2) to determine T2-SM index (SMI) thresholds for sarcopenia, and investigate the impact of low T2-SMI on overall survival (OS), and weight loss during radiotherapy (RT).

METHODS

Adult patients with newly diagnosed HNC with a diagnostic PET-CT or RT planning CT scan were included. SM was analysed at T2 and a model applied to predict SM at L3. T2-SMI thresholds for sarcopenia were established with predicted measures, stratified by BMI and sex. Impact of sarcopenia and low T2-SMI on OS and weight loss during RT was investigated.

RESULTS

A total of 361 scans were analysed (84% males, 54% oropharynx tumours). Sarcopenia was found in 49%, demonstrating worse OS (p = 0.037). T2-SMI cutoff values were: females-74 cm2/m2 [area under the curve (AUC): 0.89 (95%CI 0.80-0.98)], males (BMI < 25)-63 cm2/m2 [AUC 0.93 (95%CI 0.89-0.96)], males (BMI ≥ 25)-88cm2/m2 [AUC 0.86 (95%CI 0.78-0.93)]. No difference in OS with T2-SMI categories. Lowest T2-SMI quartile of < 63 cm2/m2 demonstrated worse OS (p = 0.017). Weight loss during RT was higher in patients; who were not sarcopenic (6.2% vs 4.9%, p = 0.023); with higher T2-SMI (6.3% vs 4.9%, p = 0.014) and; in the highest quartiles (3.6% vs 5.7% vs 7.2%, p < 0.001).

CONCLUSIONS

These T2-SMI thresholds are effective in assessing CT-defined sarcopenia in HNC. Further assessment of clinical application is warranted.

Who may benefit from an increased gonadotropin dosing in predicted poor responders undergoing IVF/ICSI? A secondary analysis assessing treatment selection markers of a randomized trial

OBJECTIVE

To evaluate whether we can identify patient characteristics that serve as treatment selection markers to distinguish which women with expected poor response benefit from increased dosing of follicle-stimulating hormone (FSH) in terms of improving the cumulative live birth rate compared to standard FSH dosing and which women.

STUDY DESIGN

We performed a secondary analysis of an RCT performed between March 2019 and October 2021 comparing cumulative live birth after increased dosing (N = 328) who received 225 or 300 IU/day according to their antral follicle count (AFC) and standard dosing (N = 333) who received 150 IU/day of gonadotropin.

RESULTS

The MFPI analysis showed the benefit of the increased dosing of FSH on cumulative live birth starts to emerge when women were older than 30 years (women > 30 years: 46.5 % vs. 34.2 %; adjusted relative risk (aRR) 1.32, 95 % confidence interval (95 %CI) 1.05-1.66; women ≤ 30 years: 54.7 % vs. 58.6 %; aRR 0.91, 95 % CI 0.72-1.14; p for interaction 0.019). Only those who had AFC between 1 and 3 benefited from the increased FSH dose (AFC 1-3: 38.5 % vs. 6.5 %; aRR 5.88, 95 % CI 1.50-23.15; AFC 4-9: 50.3 % vs. 46.0 %; aRR 1.08, 95 % CI 0.92-1.27; p for interaction 0.023). Expected poor responders defined by the Bologna criteria and POSEIDON criteria did not significantly benefit from the increased dosing of FSH.

CONCLUSIONS

Women who are aged >30 years or have AFC 1-3 are likely to benefit from increased dosing of FSH by having a higher cumulative live birth rate.

Development and external validation of dual online tools for prognostic assessment in elderly patients with high-grade glioma: a comprehensive study using SEER and Chinese cohorts

Background

Elderly individuals diagnosed with high-grade gliomas frequently experience unfavorable outcomes. We aimed to design two web-based instruments for prognosis to predict overall survival (OS) and cancer-specific survival (CSS), assisting clinical decision-making.

Methods

We scrutinized data from the SEER database on 5,245 elderly patients diagnosed with high-grade glioma between 2000-2020, segmenting them into training (3,672) and validation (1,573) subsets. An additional external validation cohort was obtained from our institution. Prognostic determinants were pinpointed using Cox regression analyses, which facilitated the construction of the nomogram. The nomogram's predictive precision for OS and CSS was gauged using calibration and ROC curves, the C-index, and decision curve analysis (DCA). Based on risk scores, patients were stratified into high or low-risk categories, and survival disparities were explored.

Results

Using multivariate Cox regression, we identified several prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in elderly patients with high-grade gliomas, including age, tumor location, size, surgical technique, and therapies. Two digital nomograms were formulated anchored on these determinants. For OS, the C-index values in the training, internal, and external validation cohorts were 0.734, 0.729, and 0.701, respectively. We also derived AUC values for 3-, 6-, and 12-month periods. For CSS, the C-index values for the training and validation groups were 0.733 and 0.727, with analogous AUC metrics. The efficacy and clinical relevance of the nomograms were corroborated via ROC curves, calibration plots, and DCA for both cohorts.

Conclusion

Our investigation pinpointed pivotal risk factors in elderly glioma patients, leading to the development of an instrumental prognostic nomogram for OS and CSS. This instrument offers invaluable insights to optimize treatment strategies.

Temporal effect of imatinib adherence on time to remission in chronic myeloid leukemia patients

INTRODUCTION

Adherence to imatinib in chronic myeloid leukemia (CML) patients is estimated to be as low as 70% despite its clinical benefit, and our understanding of the impact of nonadherence in this population is limited. This study presents a novel application of the Alternating Conditional Estimation (ACE) algorithm in newly diagnosed CML patients to map the full dose-response curve (DRC) and determine how the strength of this curve varies over time.

METHODS

We applied the ACE algorithm alongside a backward elimination procedure to detect the presence of time dependence and nonlinearity in the relationship between imatinib adherence and time-to-remission. An extended Cox model allowing for the flexible modeling of identified effects via unpenalized B-splines was subsequently fit and assessed.

RESULTS

The substantial improvement in model fit associated with the extended Cox approach suggests that traditional Cox proportional hazards model assumptions do not hold in this setting. Results indicate that the DRC for imatinib is non-linearly increasing, with an attenuated effect above a 74% adherence rate. The strength of this effect on remission varied over time and was strongest in the initial months of treatment, reaching a peak around 90 days post-initiation (log hazard ratio: 2.12, 95% confidence interval: 1.47 to 2.66).

CONCLUSION

Most patients that achieved remission did so by 4 months (120 days) with consistently high adherence, suggesting that this could be a critical time and duration for realizing treatment benefit and patient monitoring. Findings regarding the relationship between adherence and remission can additionally help guide the design of future studies.

Prediction models in prehospital and emergency medicine research: How to derive and internally validate a clinical prediction model

Clinical prediction models are created to help clinicians with medical decision making, aid in risk stratification, and improve diagnosis and/or prognosis. With growing availability of both prehospital and in-hospital observational registries and electronic health records, there is an opportunity to develop, validate, and incorporate prediction models into clinical practice. However, many prediction models have high risk of bias due to poor methodology. Given that there are no methodological standards aimed at developing prediction models specifically in the prehospital setting, the objective of this paper is to describe the appropriate methodology for the derivation and validation of clinical prediction models in this setting. What follows can also be applied to the emergency medicine (EM) setting. There are eight steps that should be followed when developing and internally validating a prediction model: (1) problem definition, (2) coding of predictors, (3) addressing missing data, (4) ensuring adequate sample size, (5) variable selection, (6) evaluating model performance, (7) internal validation, and (8) model presentation. Subsequent steps include external validation, assessment of impact, and cost-effectiveness. By following these steps, researchers can develop a prediction model with the methodological rigor and quality required for prehospital and EM research.

Changes in Sarcopenia Status Predict Survival Among Patients with Resectable Esophageal Cancer

BACKGROUND

Sarcopenia is a predictor of survival in patients with esophageal cancer. The objective of this research was to obtain insight into how changes in sarcopenia influence survival in resectable esophageal cancer.

PATIENTS AND METHODS

A retrospective cohort of patients with esophageal cancer undergoing tri-modality therapy was selected. Body composition parameters from the staging, post-neoadjuvant, and 1-year surveillance computed tomography (CT) scans were calculated. Overall survival (OS) and disease-free survival (DFS) were evaluated using the Kaplan-Meier method and log-rank test, as well as multivariable Cox-proportional hazards models.

RESULTS

Of 141 patients, 118 had images at all three timepoints. The median DFS and OS were 33.2 [95% confidence interval (CI) 19.1-73.7] and 34.5 (95% CI 23.1-57.6) months, respectively. Sarcopenia classified by the staging CT was present in 20 (17.0%) patients. This changed to 45 (38.1%) patients by the post-neoadjuvant scan, and 44 (37.3%) by the surveillance scan. In multivariable analysis, sarcopenia at the post-neoadjuvant scan was significantly associated with OS [hazards ratio (HR) 2.65, 95% CI 1.59-4.40; p < 0.001] and DFS (HR 1.80, 95% CI 1.03-3.13; p = 0.038). The net change in skeletal muscle index was associated with OS (HR 0.93, 95% CI 0.90-0.97; p < 0.001) and DFS (HR 0.94, 95% CI 0.91-0.98; p = 0.001).

CONCLUSIONS

Patients who develop sarcopenia as a consequence of skeletal muscle wasting during neoadjuvant therapy are at risk for worse DFS and OS. Patients who have a net loss of muscle over time may be at high risk for early disease recurrence.

The association between knee muscle performance and clinical outcomes of knee function 1-4 years after a sport-related knee joint injury

OBJECTIVE

Estimate the association between index leg knee muscle strength and rate of torque development (RTD), and self-reported and performance-based (i.e., hop) knee function in persons 1-4 years after a sport-related knee joint injury.

METHODS

Data were collected at baseline of a clinical trial. Assessments included the Knee injury and Osteoarthritis Outcome Score Sport & Recreation subscale (KOOSsport), 6-m timed hop (TH), and peak concentric isokinetic knee extensor and flexor torque and isometric RTD. Associations between peak torque and RTD with KOOSsport and TH were assessed using multivariable regression with nonlinear transformations.

RESULTS

53 participants (64.2% female) were included. Knee extensor peak torque was nonlinearly related to TH time, with a strong inverse relationship at lower torque values that changed as torque increased. Results were inconsistent for flexor peak torque, extensor RTD and flexor RTD, with inconsistencies in relationship shape and estimates of association between primary and sensitivity analyses. There was no association between strength/RTD and KOOSsport.

CONCLUSION

There was a nonlinear relationship between knee extensor strength and hop function, with lower strength being associated with a stronger relationship. As strength values increased, the relationship attenuated. Knee extensor and flexor strength, or RTD, were not associated with self-reported function.

Longitude-based time zone partitions and rates of suicide

BACKGROUND

Increasing evidence suggests that conditions with decreased morning and increased evening light exposure, including shift work, daylight-saving time, and eveningness, are associated with elevated mortality and suicide risk. Given that the alignment between the astronomical, biological, and social time varies across a time zone, with later-shifted daylight exposure in the western partition, we hypothesized that western time zone partitions would have higher suicide rates than eastern partitions.

METHODS

United States (U.S.) county-level suicide and demographic data, from 2010 to 2018, were obtained from a Centers for Disease Control database. Using longitude and latitude, counties were sorted into the western, middle, or eastern partition of their respective time zones, as well as the northern and southern halves of the U.S. Linear regressions were used to estimate the associations between suicide rates and time zone partitions, adjusting for gender, race, ethnicity, age group, and unemployment rates.

RESULTS

Data were available for 2872 counties. Across the U.S., western partitions had statistically significantly higher rates of suicide compared to eastern partitions and averaged up to two additional yearly deaths per 100,000 people (p < .001).

LIMITATIONS

Ecological design and limited adjustment for socioeconomic factors.

CONCLUSIONS

To our knowledge, this is the first study of the relationship between longitude-based time zone partitions and suicide. The results were consistent with the hypothesized elevated suicide rates in the western partitions, and concordant with previous reports on cancer mortality and transportation fatalities. The next step is to retest the hypothesis with individual-level data, accounting for latitude, photoperiodic changes, daylight-saving time, geoclimatic variables, physical and mental health indicators, as well as socioeconomic adversity and protection.

Evidence of questionable research practices in clinical prediction models

BACKGROUND

Clinical prediction models are widely used in health and medical research. The area under the receiver operating characteristic curve (AUC) is a frequently used estimate to describe the discriminatory ability of a clinical prediction model. The AUC is often interpreted relative to thresholds, with "good" or "excellent" models defined at 0.7, 0.8 or 0.9. These thresholds may create targets that result in "hacking", where researchers are motivated to re-analyse their data until they achieve a "good" result.

METHODS

We extracted AUC values from PubMed abstracts to look for evidence of hacking. We used histograms of the AUC values in bins of size 0.01 and compared the observed distribution to a smooth distribution from a spline.

RESULTS

The distribution of 306,888 AUC values showed clear excesses above the thresholds of 0.7, 0.8 and 0.9 and shortfalls below the thresholds.

CONCLUSIONS

The AUCs for some models are over-inflated, which risks exposing patients to sub-optimal clinical decision-making. Greater modelling transparency is needed, including published protocols, and data and code sharing.

Radiosensitivity Index is Not Fit to be Used for Dose Adjustments: A Pan-Cancer Analysis

AIMS

To explore the preclinical and latest clinical evidence of the radiation sensitivity signature termed 'radiosensitivity index' (RSI), to assess its suitability as an input into dose-adjustment algorithms.

MATERIALS AND METHODS

The original preclinical test-set data from the publication where RSI was derived were collected and reanalysed by comparing the observed versus predicted survival fraction at 2 Gy (SF2). In addition, the predictive capability of RSI was also compared to random guessing. Clinical data were collected from a recently published dataset that included RSI values, overall survival outcomes, radiotherapy dose and tumour site for six cancers (glioma, triple-negative breast, endometrial, melanoma, pancreatic and lung cancer). Cox proportional hazards models were used to assess: (i) does adjusting for RSI elucidate a dose response and (ii) does an interaction between RSI and dose exist with good precision.

RESULTS

Preclinically, RSI showed a negative correlation (Spearman's rho = -0.61) between observed and predicted SF2, which remained negative after removing leukaemia cell lines. Furthermore, random guesses showed better correlation to SF2 than RSI, 98% of the time on the full dataset and 80% after removing leukaemia cell lines. The preclinical data show that RSI does not explain the variance in SF2 better than random guessing. Clinically, a dose response was not seen after adjusting for RSI (hazard ratio = 1.00, 95% confidence interval 0.97-1.04; P = 0.876) and no evidence of an interaction between RSI and dose was found (P = 0.844).

CONCLUSIONS

These results suggest that RSI does not explain a sufficient amount of the outcome variance to be used within dose-adjustment algorithms.

Identifying radiomics signatures in body composition imaging for the prediction of outcome following pancreatic cancer resection

Background

Computerized radiological image analysis (radiomics) enables the investigation of image-derived phenotypes by extracting large numbers of quantitative features. We hypothesized that radiomics features may contain prognostic information that enhances conventional body composition analysis. We aimed to investigate whether body composition-associated radiomics features hold additional value over conventional body composition analysis and clinical patient characteristics used to predict survival of pancreatic ductal adenocarcinoma (PDAC) patients.

Methods

Computed tomography images of 304 patients undergoing elective pancreatic cancer resection were analysed. 2D radiomics features were extracted from skeletal muscle and subcutaneous and visceral adipose tissue (SAT and VAT) compartments from a single slice at the third lumbar vertebra. The study population was randomly split (80:20) into training and holdout subsets. Feature ranking with Least Absolute Shrinkage Selection Operator (LASSO) followed by multivariable stepwise Cox regression in 1000 bootstrapped re-samples of the training data was performed and tested on the holdout data. The fitted regression predictors were used as "scores" for a clinical (C-Score), body composition (B-Score), and radiomics (R-Score) model. To stratify patients into the highest 25% and lowest 25% risk of mortality compared to the middle 50%, the Harrell Concordance Index was used.

Results

Based on LASSO and stepwise cox regression for overall survival, ASA ≥3 and age were the most important clinical variables and constituted the C-score, and VAT-index (VATI) was the most important body composition variable and constituted the B-score. Three radiomics features (SATI_original_shape2D_Perimeter, VATI_original_glszm_SmallAreaEmphasis, and VATI_original_firstorder_Maximum) emerged as the most frequent set of features and yielded an R-Score. Of the mean concordance indices of C-, B-, and R-scores, R-score performed best (0.61, 95% CI 0.56-0.65, p<0.001), followed by the C-score (0.59, 95% CI 0.55-0.63, p<0.001) and B-score (0.55, 95% CI 0.50-0.60, p=0.03). Kaplan-Meier projection revealed that C-, B, and R-scores showed a clear split in the survival curves in the training set, although none remained significant in the holdout set.

Conclusion

It is feasible to implement a data-driven radiomics approach to body composition imaging. Radiomics features provided improved predictive performance compared to conventional body composition variables for the prediction of overall survival of PDAC patients undergoing primary resection.

Multi-Quantitative Assessment of AS-OCTA Complemented AS-OCT for Monitoring Filtering Bleb Function After Trabeculectomy

Purpose

The purpose of this study was to explore a quantitative grading system of the filtering bleb combined anterior segment optical coherence tomography angiography (AS-OCTA) vascular features and optical coherence tomography (OCT) morphological features.

Methods

One hundred three eyes of 103 patients diagnosed with primary open-angle glaucoma and undergone trabeculectomy over 6 months were divided into success and failure groups according to postoperative intraocular pressure (IOP) level. Vessel density (VD) and vessel diameter index (VDI) were examined by AS-OCTA. Bleb's morphology, including bleb height (BH), and microcyst-structure (MCS) were detected by AS-OCT. Multi-vascular model score (MVMS) was calculated by comprehensive factor analysis, and the comprehensive grading system (MVMS-MCS-BH) was analyzed by linear regression. The efficiency our method was verified by receiver operating characteristic (ROC) analysis.

Results

The VD and VDI were higher in the failure group and closely related to post-trabeculectomy IOP (all P = 0.000). The MVMS was mostly consisted of VD in all regions, and VDIs of nasal, central, and temporal positions in sequence. MVMS ≥0, BH <1.33, and non-MCS were significantly associated with IOP increasing (coefficient = -3.23, -3.69, and 8.10, all P = 0.000). MVMS-BH-MCS got a higher area under curve (AUC), sensitivity, and specificity (0.92, 100%, and 80.30%) than the slit-lamp method (0.62, 72.20%, and 46.43%, respectively).

Conclusions

The quantitative vascular characteristics detected by AS-OCTA were significant for the bleb monitor. The MVMS-BH-MCS grading system had achieved outstanding accuracy in reflecting the surgical results.

Translational Relevance

The multi-vascular biomarker and comprehensive evaluation combined vascular and morphological parameters yield useful information on surgical outcomes, and help ophthalmologists to monitor patients effectively.

The presentation of results from studies in clinical haematology

Research is based on trying to find answers to specific questions or to test hypotheses. Studies are thus undertaken to generate data which, with appropriate statistical methods, will help to determine the validity of the science under investigation. The aim of this paper is not to provide answers on which statistical methods to use, but will concentrate on suggesting the best ways of presenting the results of appropriately analysed data. And presentation is the key, because however well conducted and analysed a study may be, incorrect or inappropriate presentation of the findings will severely hamper its publication potential. With illustrative examples, the fundamentals required in the presentation of study objectives, population selection, description of characteristics and missing values, survival analyses, unadjusted analyses, multivariate regression models and matched pair analyses, are presented.

Usefulness and real-world outcomes of next generation sequencing testing in patients with cancer: an observational study on the impact of selection based on clinical judgement

Background

Next Generation Sequencing (NGS) panels are increasingly used in advanced patients with cancer to guide therapy. There is, however, controversy about when should these panels be used, and about their impact on the clinical course.

Methods

In an observational study of 139 patients with cancer having an NGS test [from January 1st, 2017 to December 30th, 2020, in two hospitals (Hospital Universitario de La Princesa and Hospital Universitario Quironsalud Madrid) from Spain], we evaluated whether the clinical course (progression-free survival, PFS) was influenced by drug-based criteria [druggable alterations, receiving a recommended drug, having a favourable ESCAT category (ESMO Scale for Clinical Actionability of molecular Targets)] or clinical judgement criteria.

Findings

In 111 of 139 cases that were successfully profiled, PFS was not significantly influenced by either having druggable alterations [median PFS for patients with druggable alterations was 170 (95% C.I.: 139-200) days compared to 299 (95% C.I.: 114-483) for those without; p = 0.37], receiving a proposed matching agent [median PFS for patients receiving a genomics-informed drug was 195 days (95% C.I.: 144-245), compared with 156 days for those that did not (95% C.I.: 85-226); p = 0.50], or having favourable ESCAT categories [median PFS for patients with ESCAT I-III was 183 days (95% C.I.: 104-261), compared with 180 (95% C.I.:144-215) for patients with ESCAT IV-X; p = 0.87]. In contrast, NGS testing performed within clinical judgement showed a significantly improved PFS [median PFS for patients that were profiled under the recommended scenarios was 319 days (95% C.I.: 0-658), compared to 123 days (95% C.I.: 89-156) in the non-recommended categories; p = 0.0020].

Interpretation

According to our data, real-world outcomes after NGS testing provide evidence of the benefit of clinical judgement in patients with either advanced cancers that routinely need multiple genetic markers, patients with advanced rare cancers, or patients that are screened for molecular clinical trials. By contrast, NGS does not seem to be valuable when performed in cases with a poor PS, rapidly progressing cancer, short expected lifetime, or cases with no standard therapeutic options.

Funding

RC, NR-L and MQF are recipients of the PMP22/00032 grant, funded by the ISCIII and co-funded by the European Regional Development Fund (ERDF). The study also received funds from the CRIS Contra el Cancer Foundation.

Effects of influential points and sample size on the selection and replicability of multivariable fractional polynomial models

BACKGROUND

The multivariable fractional polynomial (MFP) approach combines variable selection using backward elimination with a function selection procedure (FSP) for fractional polynomial (FP) functions. It is a relatively simple approach which can be easily understood without advanced training in statistical modeling. For continuous variables, a closed test procedure is used to decide between no effect, linear, FP1, or FP2 functions. Influential points (IPs) and small sample sizes can both have a strong impact on a selected function and MFP model.

METHODS

We used simulated data with six continuous and four categorical predictors to illustrate approaches which can help to identify IPs with an influence on function selection and the MFP model. Approaches use leave-one or two-out and two related techniques for a multivariable assessment. In eight subsamples, we also investigated the effects of sample size and model replicability, the latter by using three non-overlapping subsamples with the same sample size. For better illustration, a structured profile was used to provide an overview of all analyses conducted.

RESULTS

The results showed that one or more IPs can drive the functions and models selected. In addition, with a small sample size, MFP was not able to detect some non-linear functions and the selected model differed substantially from the true underlying model. However, when the sample size was relatively large and regression diagnostics were carefully conducted, MFP selected functions or models that were similar to the underlying true model.

CONCLUSIONS

For smaller sample size, IPs and low power are important reasons that the MFP approach may not be able to identify underlying functional relationships for continuous variables and selected models might differ substantially from the true model. However, for larger sample sizes, a carefully conducted MFP analysis is often a suitable way to select a multivariable regression model which includes continuous variables. In such a case, MFP can be the preferred approach to derive a multivariable descriptive model.

Clinicopathological and Prognostic Characteristics in Spinal Chondroblastomas: A Pooled Analysis of Individual Patient Data From a Single Institute and 27 Studies

STUDY DESIGN

Retrospective pooled analysis of individual patient data.

OBJECTIVES

Spinal chondroblastoma (CB) is a very rare pathology and its clinicopathological and prognostic features remain unclear. Here, we sought to characterize the clinicopathological data of a large spinal CB cohort and determine factors affecting the local recurrence-free survival (LRFS) and overall survival (OS) of patients.

METHODS

Electronic searches using Medline, Embase, Google Scholar and Wanfang databases were performed to identify eligible studies per predefined criteria. A retrospective review was also conducted to include additional patients at our center.

RESULTS

Twenty-seven studies from the literature and 8 patients from our local institute were identified, yielding a total of 61 patients for analysis. Overall, there were no differences in clinicopathological characteristics between the local and literature cohorts, except for absence or presence of spinal canal invasion by tumor on imagings and chicken-wire calcification in tumor tissues. Univariate Kaplan-Meier analysis revealed that previous treatment, preoperative or postoperative neurological deficits, type of tumor resection, secondary aneurysmal bone cyst (ABC), chicken-wire calcification and radiotherapy correlated closely with LRFS, though only type of tumor resection, chicken-wire calcification and radiotherapy were predictive of outcome based on multivariate Cox analysis. Analyzing OS, we found that a history of preoperative treatment, concurrent ABC, chicken-wire calcification, type of tumor resection and adjuvant radiotherapy had a significant association with survival, whereas only type of tumor resection remained statistically significant after adjusting for other covariables.

CONCLUSION

These data may be helpful in prognostic risk stratification and individualized therapy decision making for patients.

Predictors of Surgical Site Infection in Pediatric Cochlear Implantation

OBJECTIVE

To determine risk factors for surgical site infection (SSI) after cochlear implantation (CI) in pediatric patients.

STUDY DESIGN

Case-control study.

SETTING

A total of 150 hospitals contributing data to the ACS-NSQIP Pediatric database (American College of Surgeons National Surgical Quality Improvement Program) in North America and worldwide.

METHODS

Pediatric patients (aged <18 years) undergoing CI during the years 2012 to 2017 were identified in the ACS-NSQIP Pediatric database. Uni- and multivariable logistic regression analyses were used to determine the odds ratios (ORs) of SSI (including superficial incisional, deep incisional, organ/space) occurring up to 30 days postoperatively.

RESULTS

A total of 79 SSIs occurred over a 5-year period (n = 5146). Longer operative time significantly increased the odds of SSI (OR, 1.965; 95% CI, 1.205-3.289). Younger age was also found to raise the odds of SSI, with decreased odds associated with each 6-month increase in age (OR, 0.887; 95% CI, 0.814-0.958).

CONCLUSION

Longer operative time and younger age appear to significantly increase the odds of SSI in pediatric CI. Body mass index, recent steroid use, American Society of Anesthesiologists class, bilateral vs unilateral implantation, and hospital length of stay do not appear to significantly influence SSI risk. These findings must be interpreted in the context of the limitations inherent to adverse events reporting, which are mitigated by the stringent manner of data collection by the ACS-NSQIP, and those inherent to the definition of SSI. Future prospective studies should investigate the impact of reducing operative time on the risk of SSI and other complications in pediatric CI.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

Association between intraoperative oliguria and postoperative acute kidney injury in non-cardiac surgical patients: a systematic review and meta-analysis

PURPOSE

This systematic review and meta-analysis aimed to evaluate the association between intraoperative oliguria and the risk of postoperative acute kidney injury (AKI) in patients undergoing non-cardiac surgery.

METHODS

The MEDLINE and EMBASE databases were searched up to August 2022 for studies in adult patients undergoing non-cardiac surgery, where the association between intraoperative urine output and the risk of postoperative AKI was assessed. Both randomised and non-randomised studies were eligible for inclusion. Study selection and risk of bias assessment were independently performed by two investigators. The risk of bias was evaluated using the Newcastle-Ottawa scale. We performed meta-analysis of the reported multivariate adjusted odds ratios for the association between intraoperative oliguria (defined as urine output < 0.5 mL/kg/hr) and the risk of postoperative AKI using the inverse-variance method with random effects models. We conducted sensitivity analyses using varying definitions of oliguria as well as by pooling unadjusted odds ratios to establish the robustness of the primary meta-analysis. We also conducted subgroup analyses according to surgery type and definition of AKI to explore potential sources of clinical or methodological heterogeneity.

RESULTS

Eleven studies (total 49,252 patients from 11 observational studies including a post hoc analysis of a randomised controlled trial) met the selection criteria. Seven of these studies contributed data from a total 17,148 patients to the primary meta-analysis. Intraoperative oliguria was associated with a significantly elevated risk of postoperative AKI (pooled adjusted odds ratio [OR] 1.74; 95% confidence interval [CI] 1.36-2.23, p < 0.0001, 8 studies). Sensitivity analyses supported the robustness of the primary meta-analysis. There was no evidence of any significant subgroup differences according to surgery type or definition of AKI.

CONCLUSIONS

This study demonstrated a significant association between intraoperative oliguria and the risk of postoperative AKI, regardless of the definitions of oliguria or AKI used. Further prospective and multi-centre studies using standardised definitions of intraoperative oliguria are required to define the thresholds of oliguria and establish strategies to minimise the risk of AKI.

Prediction models for postoperative delirium after cardiac surgery: Systematic review and critical appraisal

BACKGROUND

Many studies have developed or validated prediction models to estimate the risk of delirium after cardiac surgery, but the quality of the model development and model applicability remain unknown.

OBJECTIVES

To systematically review and critically evaluate currently available prediction models for delirium after cardiac surgery.

DATA SOURCES

PubMed, EMBASE, and MEDLINE were systematically searched. This systematic review was registered in PROSPERO (Registration ID: CRD42021251226).

STUDY SELECTION

Prospective or retrospective cohort studies were considered eligible if they developed or validated prediction models or scoring systems for delirium in the ICU. We included studies involving adults (age ≥18 years) undergoing cardiac surgery and excluded studies that did not validate a prediction model.

DATA EXTRACTION

Data extraction was independently performed by two authors using a standardized data extraction form based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies checklist. Quality of the models was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST).

DATA SYNTHESIS

Of 5469 screened studies, 13 studies described 10 prediction models. The postoperative delirium incidence varied from 11.3 % to 51.6 %. The most frequently used predictors were age and cognitive impairment. The reported areas under the curve or C-statistics were between of 0.74 and 0.91 in the derivation set. The reported AUCs in the external validation set were between 0.54 and 0.90. All the studies had a high risk of bias, mainly owing to poor reporting of the outcome domain and analysis domain; 10 studies were of high concern regarding applicability.

CONCLUSIONS

The current models for predicting postoperative delirium in the ICU after cardiac surgery had a high risk of bias according to the PROBAST. Future studies should focus on improving current prediction models or developing new models with rigorous methodology.

Outcomes in small children on Berlin Heart EXCOR support: age and body surface area as clinical predictive factors

OBJECTIVES

The Berlin Heart EXCOR (BHE) offers circulatory support across all paediatric ages. Clinically, the necessary care and the outcomes differ in various age groups. The EUROMACS database was used to study age- and size-related outcomes for this specific device.

METHODS

All patients <19 years of age from the EUROMACS database supported with a BHE between 2000 and November 2021 were included. Maximally selected rank statistics were used to determine body surface area (BSA) cut-off values. Multivariable Cox proportional hazard regression using ridge penalization was performed to identify factors associated with outcomes.

RESULTS

In total, 303 patients were included [mean age: 2.0 years (interquartile range: 0.6-8.0, males: 48.5%)]. Age and BSA were not significantly associated with mortality (n = 74, P = 0.684, P = 0.679). Factors associated with a transplant (n = 175) were age (hazard ratio 1.07, P = 0.006) and aetiology other than congenital heart disease (hazard ratio 1.46, P = 0.020). Recovery rates (n = 42) were highest in patients with a BSA of <0.53 m2 (21.8% vs 4.3-7.6% at 1 year, P = 0.00534). Patients with a BSA of ≥0.73 m2 had a lower risk of early pump thrombosis but a higher risk of early bleeding compared to children with a BSA of <0.73 m2.

CONCLUSIONS

Mortality rates in Berlin Heart-supported patients cannot be predicted by age or BSA. Recovery rates are remarkably high in the smallest patient category (BSA <0.53 m2). This underscores that the BHE is a viable therapeutic option, even for the smallest and youngest patients.

A comparison of joint dichotomization and single dichotomization of interacting variables to discriminate a disease outcome

Dichotomization is often used on clinical and diagnostic settings to simplify interpretation. For example, a person with systolic and diastolic blood pressure above 140 over 90 may be prescribed medication. Blood pressure as well as other factors such as age and cholesterol and their interactions may lead to increased risk of certain diseases. When using a dichotomized variable to determine a diagnosis, if the interactions with other variables are not considered, then an incorrect threshold for the continuous variable may be selected. In this paper, we compare single dichotomization with joint dichotomization; the process of simultaneously optimizing cutpoints for multiple variables. A simulation study shows that simultaneous dichotomization of continuous variables is more accurate in recovering both 'true' thresholds given they exist.

Mid-Regional Pro-Adrenomedullin as a Prognostic Factor for Severe COVID-19 ARDS

Mid-regional proadrenomedullin (MR-proADM) protects against endothelial permeability and has been associated with prognosis in bacterial sepsis. As endothelial dysfunction is central in the pathophysiology of severe SARS-CoV-2 infection, we sought to evaluate MR-proADM both as a prognostic biomarker and as a marker of bacterial superinfection. Consecutive patients admitted to the ICU for severe SARS-CoV-2 pneumonia were prospectively included and serum was bio-banked on days 1, 3, and 7. MR-proADM levels were measured blindly from clinical outcomes in batches at the end of follow-up. Among the 135 patients included between April 2020 and May 2021, 46 (34.1%) had died at day 60. MR-proADM levels on days 1, 3, and 7 were significantly higher in day-60 non-survivors. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve (0.744, p < 0.001) of day-1 MR-proADM compared favorably with the AUC ROC curve of day-1 procalcitonin (0.691, p < 0.001). Serial MR-proADM measurements on days 3 and 7 may add prognostic information. After adjusting for CRP, LDH, and lymphocyte values, day-1 MR-proADM remained significantly associated with day-60 mortality. MR-proADM concentrations were significantly higher in patients with respiratory superinfections (on days 3 and 7) and bloodstream infections (on days 1, 3, and 7) than in patients without infection. Our results suggest that MR-proADM is a good predictor of outcome in severe SARS-CoV-2 infection and could be a useful tool to assess bacterial superinfection in COVID-19 patients.

Modeling non-linear relationships in epidemiological data: The application and interpretation of spline models

Objective

Traditional methods to deal with non-linearity in regression analysis often result in loss of information or compromised interpretability of the results. A recommended but underutilized method for modeling non-linear associations in regression models is spline functions. We explain spline functions in a non-mathematical way and illustrate the application and interpretation to an empirical data example.

Methods

Using data from the Amsterdam Growth and Health Longitudinal Study, we examined the non-linear relationship between the sum of four skinfolds and VO2max, which are measures of body fat and cardiorespiratory fitness, respectively. We compared traditional methods (i.e., quadratic regression and categorization) to spline methods [1- and 3-knot linear spline (LSP) models and a 3-knot restricted cubic spline (RCS) model] in terms of the interpretability of the results and their explained variance (r a d j 2 ).

Results

The spline models fitted the data better than the traditional methods. Increasing the number of knots in the LSP model increased the explained variance (from r a d j 2 = 0 . 578 for the 1-knot model to r a d j 2 = 0 . 582 for the 3-knot model). The RCS model fitted the data best (r a d j 2 = 0 . 591 ), but results in regression coefficients that are harder to interpret.

Conclusion

Spline functions should be considered more often as they are flexible and can be applied in commonly used regression analysis. RCS regression is generally recommended for prediction research (i.e., to obtain the predicted outcome for a specific exposure value), whereas LSP regression is recommended if one is interested in the effects in a population.

Risk stratification system and web-based nomogram constructed for predicting the overall survival of primary osteosarcoma patients after surgical resection

Background

Previous prediction models of osteosarcoma have not focused on survival in patients undergoing surgery, nor have they distinguished and compared prognostic differences among amputation, radical and local resection. This study aimed to establish and validate the first reliable prognostic nomogram to accurately predict overall survival (OS) after surgical resection in patients with osteosarcoma. On this basis, we constructed a risk stratification system and a web-based nomogram.

Methods

We enrolled all patients with primary osteosarcoma who underwent surgery between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. In patients with primary osteosarcoma after surgical resection, univariate and multivariate cox proportional hazards regression analyses were utilized to identify independent prognostic factors and construct a novel nomogram for the 1-, 3-, and 5-year OS. Then the nomogram's predictive performance and clinical utility were evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Result

This study recruited 1,396 patients in all, with 837 serving as the training set (60%) and 559 as the validation set (40%). After COX regression analysis, we identified seven independent prognostic factors to develop the nomogram, including age, primary site, histological type, disease stage, AJCC stage, tumor size, and surgical method. The C-index indicated that this nomogram is considerably more accurate than the AJCC stage in predicting OS [Training set (HR: 0.741, 95% CI: 0.726–0.755) vs. (HR: 0.632, 95% CI: 0.619–0.645); Validation set (HR: 0.735, 95% CI: 0.718–0.753) vs. (HR: 0.635, 95% CI: 0.619–0.652)]. Moreover, the area under ROC curves, the calibration curves, and DCA demonstrated that this nomogram was significantly superior to the AJCC stage, with better predictive performance and more net clinical benefits.

Conclusion

This study highlighted that radical surgery was the first choice for patients with primary osteosarcoma since it provided the best survival prognosis. We have established and validated a novel nomogram that could objectively predict the overall survival of patients with primary osteosarcoma after surgical resection. Furthermore, a risk stratification system and a web-based nomogram could be applied in clinical practice to assist in therapeutic decision-making.

Prognostic and predictive roles of cancer stem cell markers in head and neck squamous cell carcinoma patients receiving chemoradiotherapy with or without nimotuzumab

BACKGROUND

Anti-EGFR-based therapies have limited success in HNSCC patients. Predictive biomarkers are needed to identify the patients most likely to benefit from these therapies. Here, we present predictive and prognostic associations of different cancer stem cell markers in HPV-negative locally advanced (LA) HNSCC patients.

METHODS

Pretreatment tumour tissues of 404 HPV-negative LA-HNSCCs patients, a subset of-phase 3-randomised study comparing cisplatin-radiation(CRT) and nimotuzumab plus cisplatin-radiation(NCRT) were examined. The expression levels of CD44, CD44v6, CD98hc, ALDH1A1, SOX2 and OCT4A were evaluated using immunohistochemistry. Progression-free survival(PFS), loco-regional control(LRC),- and overall survival(OS) were estimated by Kaplan-Meier method. Hazard ratios were estimated by Cox proportional hazard models.

RESULTS

NCRT showed significantly improved OS with low membrane expression of CD44 compared to CRT [HR (95% CI) = 0.63 (0.46-0.88)]. Patients with low CD44v6 also showed better outcomes with NCRT [LRC: HR (95% CI) = 0.25 (0.10-0.62); OS: HR (95% CI) = 0.38 (0.19-0.74)]. No similar benefit with NCRT observed in patients with high CD44 or CD44v6 expression. Bootstrap resampling confirmed the predictive effect of CD44 (Interaction P = 0.015) and CD44v6 (Interaction P = 0.041) for OS. Multivariable Cox analysis revealed an independent negative prognostic role of CD98hc membrane expression for LRC [HR (95% CI) = 0.63(0.39-1.0)] and OS[HR (95% CI) = 0.62 (0.40-0.95)].

CONCLUSIONS

CD44 and CD44v6 are potential predictive biomarkers for NCRT response. CD98hc emerged as an independent negative prognostic biomarker.

CLINICAL TRIAL REGISTRATION

Registered with the Clinical Trial Registry of India (Trial registration identifier-CTRI/2014/09/004980).

Predictive scoring systems for molecular responses in persons with chronic phase chronic myeloid leukemia receiving initial imatinib therapy

It is vital for physicians and persons with chronic myeloid leukemia (CML) to accurately predict the likelihood of achieving a major molecular response (MMR) and a deep molecular response (DMR; at least MR⁴) at the start of imatinib-therapy, which could help in decision making of treatment goals and strategies. To answer this question, we interrogated data from 1369 consecutive subjects with chronic phase CML receiving initial imatinib-therapy to identify predictive co-variates. Subjects were randomly-assigned to training (n = 913) and validation (n = 456) datasets. Male sex, higher WBC concentration, lower haemoglobin concentration, higher percentage blood blasts and larger spleen size were significantly-associated with lower cumulative incidences of MMR and MR⁴ in training dataset. Using Fine-Gray model, we developed the predictive scoring systems for MMR and MR⁴ which classified subjects into the low-, intermediate- and high-risk cohorts with significantly-different cumulative incidences of MMR and MR⁴ with good predictive discrimination and accuracy in training and validation cohorts with high area under the receiver-operator characteristic curve (AUROC) values. These data may help physicians decide appropriateness of initial imatinib therapy.

Toward optimization of cyclosporine concentration target to prevent acute graft-vs-host disease following myeloablative allogeneic stem cell transplant

Despite the common use of cyclosporine (CsA) for acute graft-versus-host disease (aGVHD) prophylaxis following allogeneic stem cell transplant, the optimal CsA trough target remains unknown. Here, we report on outcomes of adult patients following myeloablative conditioning to identify an optimal CsA trough target and characterize the most relevant timeframe post-transplant for CsA trough targeting to minimize aGVHD. We retrospectively reviewed 399 consecutive patients who underwent first peripheral blood allogeneic stem cell transplant for hematological malignancies between January 2009 and December 2018. In the unadjusted and adjusted analyses, the incidence of grades 2-4 aGVHD was significantly higher among patients with an average CsA trough concentration <250 mcg/L compared to patients with an average CsA trough concentration ≥250 mcg/L during days 15-28 post-transplant (31.5% versus 18.8%, P = 0.037), with an odds ratio (OR) of 1.97 (95% confidence interval 1.04-3.71). In contrast, no correlations between CsA trough concentration and relapse, non-relapse mortality and overall survival was found. In conclusion, early post-transplant CsA trough concentrations are an important factor in the prophylaxis against aGVHD. Our findings suggest that CsA trough concentrations should be maximized between days 15-28 post-myeloablative transplant. This article is protected by copyright. All rights reserved.

Association Between the Frailty Index and Clinical Outcomes after Coronary Artery Bypass Grafting

Background

This study investigated the predictive value of the frailty index calculated using laboratory data and vital signs (FI-L) in patients who underwent coronary artery bypass grafting (CABG).

Methods

This study included 508 patients (age 67.3±9.7 years, male 78.0%) who underwent CABG between 2018 and 2021. The FI-L, which estimates patients’ frailty based on laboratory data and vital signs, was calculated as the ratio of variables outside the normal range for 32 preoperative parameters. The primary endpoints were operative and medium-term all-cause mortality. The secondary endpoints were early postoperative complications and major adverse cardiac and cerebrovascular events (MACCEs).

Results

The mean FI-L was 20.9%±10.9%. The early mortality rate was 1.6% (n=8). Postoperative complications were atrial fibrillation (n=148, 29.1%), respiratory complications (n=38, 7.5%), and acute kidney injury (n=15, 3.0%). The 1- and 3-year survival rates were 96.0% and 88.7%, and the 1- and 3-year cumulative incidence rates of MACCEs were 4.87% and 8.98%. In multivariable analyses, the FI-L showed statistically significant associations with medium-term all-cause mortality (hazard ratio [HR], 1.042; 95% confidence interval [CI], 1.010–1.076), MACCEs (subdistribution HR, 1.054; 95% CI, 1.030–1.078), atrial fibrillation (odds ratio [OR], 1.02; 95% CI, 1.002–1.039), acute kidney injury (OR, 1.06; 95% CI, 1.014–1.108), and re-operation for bleeding (OR, 1.09; 95% CI, 1.032–1.152). The minimal p-value approach showed that 32% was the best cutoff for the FI-L as a predictor of all-cause mortality post-CABG.

Conclusion

The FI-L was a significant prognostic factor related to all-cause mortality and postoperative complications in patients who underwent CABG.

Prognostic Value of Lymph Node Evaluation in Stage II Small Bowel Adenocarcinoma: An Updated Analysis of Surveillance, Epidemiology, and End Results Database

Background

Due to the lack of large-scale clinical trials, the treatment strategies of small bowel adenocarcinoma (SBA) are controversial, especially for stage II patients. According to the National Comprehensive Cancer Network (NCCN) guideline, few lymph nodes (LNs) examined (<5 for duodenum or <8 for jejunal/ileal primary location) are one of the high-risk features for stage II patients, for whom adjuvant chemotherapy is recommended. This consensus is originally drawn from data in the Surveillance, Epidemiology, and End Results Database (SEER) between 1988 and 2010. However, the surgical modalities and chemotherapy strategies changed a lot after 2004 for SBA patients. The previous data may not represent a true picture of current therapeutics. Thus, we reanalyzed the SEER database and updated the cutoff point of LN numbers resected with respect to cancer-specific survival (CSS) using the latest SEER information.

Methods

Patients diagnosed with stage II SBA and who underwent curative surgery between 2004 and 2018 were extracted from the SEER database. CSS was calculated using the Kaplan–Meier method and compared by log-rank test. Maximum survival differences based on total LNs examined for duodenal and jejunoileal tumors were determined separately with the cut-point analysis and maximum log-rank χ² statistic. A nomogram model was constructed based on the multivariate Cox analysis to predict 5‐ and 10‐year CSS and was then validated with an internal cohort.

Results

A total of 935 stage II SBA patients met the inclusion criteria. The greatest difference in survival was found in patients who had removal of at least 5 LNs for duodenal and 12 LNs for jejunoileal tumors. Multivariate Cox analysis showed that age, T stage, histology grade, primary site, and LN numbers were independent prognostic factors for survival. The C index of nomogram model was 0.701 (95% CI, 0.661–0.741, p < 0.001).

Conclusions

The number of LNs harvested is an important prognostic factor for survival in stage II SBA patients. LN number examined <5 remains a high-risk factor for duodenum, but the cutoff point for jejunal/ileal tumors should rise from 8 to 12. Appropriate radical lymphadenectomy should be performed in stage II SBA surgery.

cSurvival: a web resource for biomarker interactions in cancer outcomes and in cell lines

Survival analysis is a technique for identifying prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled &gt;11 000 adult and &gt;4000 pediatric tumor cases with clinical outcomes and multiomics approaches. This provides a resource for investigating molecular-level cancer etiologies using clinical correlations. Although cancers often arise from multiple genetic vulnerabilities and have deregulated gene sets (GSs), existing survival analysis protocols can report only on individual genes. Additionally, there is no systematic method to connect clinical outcomes with experimental (cell line) data. To address these gaps, we developed cSurvival (https://tau.cmmt.ubc.ca/cSurvival). cSurvival provides a user-adjustable analytical pipeline with a curated, integrated database and offers three main advances: (i) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (ii) survival analysis not only at the gene, but also the GS level; and (iii) integration of clinical and experimental cell line studies to generate synergistic biological insights. To demonstrate these advances, we report three case studies. We confirmed findings of autophagy-dependent survival in colorectal cancers and of synergistic negative effects between high expression of SLC7A11 and SLC2A1 on outcomes in several cancers. We further used cSurvival to identify high expression of the Nrf2-antioxidant response element pathway as a main indicator for lung cancer prognosis and for cellular resistance to oxidative stress-inducing drugs. Altogether, these analyses demonstrate cSurvival’s ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies.

Understanding the effect of categorization of a continuous predictor with application to neuro-oncology

Many neuro-oncology studies commonly assess the association between a prognostic factor (predictor) and disease or outcome, such as the association between age and glioma. Predictors can be continuous (eg, age) or categorical (eg, race/ethnicity). Effects of categorical predictors are frequently easier to visualize and interpret than effects of continuous variables. This makes it an attractive, and seemingly justifiable, option to subdivide the continuous predictors into categories (eg, age <50 years vs age ≥50 years). However, this approach results in loss of information (and power) compared to the continuous version. This review outlines the use cases for continuous and categorized predictors and provides tips and pitfalls for interpretation of these approaches.

Tumor immune cell clustering and its association with survival in African American women with ovarian cancer

New technologies, such as multiplex immunofluorescence microscopy (mIF), are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations. However, there are currently few approaches to analyze the spatial context of the TIME. Therefore, we have developed a framework for the spatial analysis of the TIME using Ripley’s K, coupled with a permutation-based framework to estimate and measure the departure from complete spatial randomness (CSR) as a measure of the interactions between immune cells. This approach was then applied to epithelial ovarian cancer (EOC) using mIF collected on intra-tumoral regions of interest (ROIs) and tissue microarrays (TMAs) from 160 high-grade serous ovarian carcinoma patients in the African American Cancer Epidemiology Study (AACES) (94 subjects on TMAs resulting in 263 tissue cores; 93 subjects with 260 ROIs; 27 subjects with both TMA and ROI data). Cox proportional hazard models were constructed to determine the association of abundance and spatial clustering of tumor-infiltrating lymphocytes (CD3+), cytotoxic T-cells (CD8+CD3+), and regulatory T-cells (CD3+FoxP3+) with overall survival. Analysis was done on TMA and ROIs, treating the TMA data as validation of the findings from the ROIs. We found that EOC patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, patients with EOC tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells had the best overall survival. Grouping women with ovarian cancer based on both cell abundance and spatial contexture showed better discrimination for survival than grouping ovarian cancer cases only by cell abundance. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the TIME. In conclusion, the application of this spatial analysis framework to the study of the TIME could lead to the identification of immune content and spatial architecture that could aid in the determination of patients that are likely to respond to immunotherapies.

New technologies, such as multiplex immunofluorescence microscopy, are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations; however, there are currently few approaches to analyze the spatial context of the TIME. Thus, we have developed a framework for the spatial analysis of the TIME and applied this method to the analysis of T-cells collected from patients with high-grade serous ovarian carcinoma in the African American Cancer Epidemiology Study. We found that patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, best survival was observed for patients with tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the ovarian TIME. The use of our framework for spatial analysis of the TIME and immune cell clustering may be applicable in other cancers and provide a novel approach to identification of biomarkers for predicting patient outcomes.

Coexpression of HHLA2 and PD-L1 on Tumor Cells Independently Predicts the Survival of Spinal Chordoma Patients

Background

Immunotherapy only achieves efficacy in some cancer patients, and less is known about other immune checkpoint molecules in chordoma. Here, we aimed to determine the expression of PD-L1, HHLA2, B7H3, IDO-1 and Galectin-9 in spinal chordoma and evaluated their association with tumor infiltrating lymphocytes (TILs), clinicopathological characteristics and survival of patients.

Methods

Using multiplexed quantitative immunofluorescence (QIF), we simultaneously measured the levels of five different immune checkpoint molecules and major TIL subsets in 92 human spinal chordoma samples.

Results

Tumor HHLA2 and PD-L1 were positive in 80.0% and 86.0% of cases, respectively. However, B7H3, IDO-1 and Galectin-9 positivity on tumor cells were only seen in 21.0% of cases, despite all showing predominantly stromal expression. Coexpression of these QIF markers in the tumor compartment was scarcely detected except for PD-L1 and HHLA2, which was observed in 69.6% of cases. While tumoral HHLA2 and stromal B7H3 expressions were associated with an aggressive tumor phenotype, suppressive immune response (specifically including elevated PD-1⁺ TILs level and decreased CD8⁺ TIL density) and poor prognosis, stromal levels of PD-L1 and Galectin-9 predicted the opposite outcomes. Importantly, HHLA2 and PD-L1 coexpression on tumor cells independently predicted both worse local recurrence-free survival and overall survival.

Conclusion

These data provide a better understanding of the immunosuppressive mechanism in chordoma and may be useful for the development of combination or novel immunotherapy approaches aiming to improve therapeutic efficacy and survival.

The value of metabolic parameters and textural analysis in predicting prognosis in locally advanced cervical cancer treated with chemoradiotherapy

Objective

The aim of the study was to assess the impact of clinical and metabolic parameters derived from ¹⁸F-FDG PET/CT (positron emission tomography–computed tomography) in patients with locally advanced cervical cancer (LACC) on prognosis.

Methods

Patients with LACC of stage IB2-IVA treated by primary radiochemotherapy followed by brachytherapy were enrolled in this retrospective study. Indexes derived from standardized uptake value (SUV), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features of the primary tumor were measured for each patient. Overall survival (OS) and recurrence-free survival (RFS) rates were calculated according to Kaplan–Meier and survival curves were compared using the log-rank test. Uni- and multivariate analyses were performed using the Cox regression model.

Results

A total of 116 patients were included. Median follow-up was 58 months (range: 1–129). A total of 36 (31%) patients died. Five-year OS and RFS rates were 69 and 60%, respectively. Univariate analyses indicated that FIGO stage, the presence of hydronephrosis, high CYFRA 21.1 levels, and textural features had a significant impact on OS and RFS. MTV as well as SCC-Ag concentration were also significantly associated with OS. On multivariate analysis, the presence of hydronephrosis, CYFRA 21.1, and sphericity were independent prognostics factors for OS and RFS. Also, SCC-Ag level, MTV, and GLZLM (gray-level zone length matrix) ZLNU (zone length non-uniformity) were significantly associated with OS.

Conclusion

Classical prognostic factors and tumor heterogeneity on pretreatment PET/CT were significantly associated with prognosis in patients with LACC.