Predictor Of Survival In Cancer Research Articles

From Head and Neck Tumour and Lymph Node Segmentation to Survival Prediction on PET/CT: An End-to-End Framework Featuring Uncertainty, Fairness, and Multi-Region Multi-Modal Radiomics.

Automatic delineation and detection of the primary tumour (GTVp) and lymph nodes (GTVn) using PET and CT in head and neck cancer and recurrence-free survival prediction can be useful for diagnosis and patient risk stratification. We used data from nine different centres, with 524 and 359 cases used for training and testing, respectively. We utilised posterior sampling of the weight space in the proposed segmentation model to estimate the uncertainty for false positive reduction. We explored the prognostic potential of radiomics features extracted from the predicted GTVp and GTVn in PET and CT for recurrence-free survival prediction and used SHAP analysis for explainability. We evaluated the bias of models with respect to age, gender, chemotherapy, HPV status, and lesion size. We achieved an aggregate Dice score of 0.774 and 0.760 on the test set for GTVp and GTVn, respectively. We observed a per image false positive reduction of 19.5% and 7.14% using the uncertainty threshold for GTVp and GTVn, respectively. Radiomics features extracted from GTVn in PET and from both GTVp and GTVn in CT are the most prognostic, and our model achieves a C-index of 0.672 on the test set. Our framework incorporates uncertainty estimation, fairness, and explainability, demonstrating the potential for accurate detection and risk stratification.

A Hybrid Deep Learning Framework with Decision-Level Fusion for Breast Cancer Survival Prediction

Because of technological advancements and their use in the medical area, many new methods and strategies have been developed to address complex real-life challenges. Breast cancer, a particular kind of tumor that arises in breast cells, is one of the most prevalent types of cancer in women and is. Early breast cancer detection and classification are crucial. Early detection considerably increases the likelihood of survival, which motivates us to contribute to different detection techniques from a technical standpoint. Additionally, manual detection requires a lot of time and effort and carries the risk of pathologist error and inaccurate classification. To address these problems, in this study, a hybrid deep learning model that enables decision making based on data from multiple data sources is proposed and used with two different classifiers. By incorporating multi-omics data (clinical data, gene expression data, and copy number alteration data) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, the accuracy of patient survival predictions is expected to be improved relative to prediction utilizing only one modality of data. A convolutional neural network (CNN) architecture is used for feature extraction. LSTM and GRU are used as classifiers. The accuracy achieved by LSTM is 97.0%, and that achieved by GRU is 97.5, while using decision fusion (LSTM and GRU) achieves the best accuracy of 98.0%. The prediction performance assessed using various performance indicators demonstrates that our model outperforms currently used methodologies.

Proposal of SVM Utility Kernel for Breast Cancer Survival Estimation.

The advancement of medical research in the field of cancer prognosis and diagnosis using various modalities has put oncologists under tremendous stress. The complexity and heterogeneity involved in multiple modalities and their significantly varied clinical outcomes make it difficult to analyze the disease and provide the correct treatment. Breast cancer is the major concern among all cancers worldwide, specifically for females. To help oncologists and cancer patients, research for breast cancer survival estimation has been proposed. It ranges from complex deep neural networks to simple and interpretable architectures. We propose a utility kernel for a support vector machine (SVM) in this article. It is a simple yet powerful function, which performs better than other popular machine learning algorithms and deep neural networks in the task of breast cancer survival prediction using the TCGA-BRCA dataset. This study validates the proposed utility kernel using four different modalities (gene expression, copy number variation, clinical, and histopathological tissue images) and their multi-modal combinations. The SVM based on our utility kernel empirically proves its efficacy by achieving the highest value on various performance measures, whereas advanced deep neural networks fail to train on small and highly imbalanced breast cancer data.

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing

Predicting short- and long-term survival of patients with cancer may improve their care. Prior predictive models either use data with limited availability or predict the outcome of only 1 type of cancer. To investigate whether natural language processing can predict survival of patients with general cancer from a patient's initial oncologist consultation document. This retrospective prognostic study used data from 47 625 of 59 800 patients who started cancer care at any of the 6 BC Cancer sites located in the province of British Columbia between April 1, 2011, and December 31, 2016. Mortality data were updated until April 6, 2022, and data were analyzed from update until September 30, 2022. All patients with a medical or radiation oncologist consultation document generated within 180 days of diagnosis were included; patients seen for multiple cancers were excluded. Initial oncologist consultation documents were analyzed using traditional and neural language models. The primary outcome was the performance of the predictive models, including balanced accuracy and receiver operating characteristics area under the curve (AUC). The secondary outcome was investigating what words the models used. Of the 47 625 patients in the sample, 25 428 (53.4%) were female and 22 197 (46.6%) were male, with a mean (SD) age of 64.9 (13.7) years. A total of 41 447 patients (87.0%) survived 6 months, 31 143 (65.4%) survived 36 months, and 27 880 (58.5%) survived 60 months, calculated from their initial oncologist consultation. The best models achieved a balanced accuracy of 0.856 (AUC, 0.928) for predicting 6-month survival, 0.842 (AUC, 0.918) for 36-month survival, and 0.837 (AUC, 0.918) for 60-month survival, on a holdout test set. Differences in what words were important for predicting 6- vs 60-month survival were found. These findings suggest that models performed comparably with or better than previous models predicting cancer survival and that they may be able to predict survival using readily available data without focusing on 1 cancer type.

Chronological horse herd optimization-based gene selection with deep learning towards survival prediction using PAN-Cancer gene-expression data

Cancer has always been one of the major hazards to human life which is also the most difficult part of human disease history. The death rate due to cancer is high. The prediction results are affected because of the major dissimilarities present in clinical results. Hence, it is necessary to enhance the accuracy of cancer survival prediction, which remains a challenging one. To defeat the challenges, this research devises a robust approach, named Deep Recurrent Neural Network-based Chronological Horse Herd Political Optimization (DRNN-based CHHPO) for survival prediction. Here, the gene selection is performed using the proposed Chronological Horse Optimization (CHO) by assuming the parameters of fitness, for example Minkowski distance plus Renyi entropy. The Horse Herd Optimization (HOA) and Chronological concept is merged to form the CHO. With the selected genes, the gene features are strengthened using technical indicators to enhance the overall process. Finally, survival prediction is completed by means of DRNN, which is trained by the CHHPO, which is the amalgamation of Political optimizer (PO) and CHO. Superior presentation with the Prediction Error (PE) and minimal Root Mean Square Error (RMSE) of 0.456 and 0.467 is accomplished by this developed technique.

Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data.

Tumor mutation burden (TMB) has been recognized as a predictive biomarker for immunotherapy response in cancer. Systematic identification of molecular features correlated with TMB is significant, although such investigation remains insufficient. We analyzed associations of somatic mutations, pathways, protein expression, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), competing endogenous RNA (ceRNA) antitumor immune signatures, and clinical features with TMB in various cancers using multi-omics datasets from The Cancer Genome Atlas (TCGA) program and datasets for cancer cohorts receiving the immune checkpoint blockade therapy. Among the 32 TCGA cancer types, melanoma harbored the highest percentage of high-TMB (≥ 10/Mb) cancers (49.4%), followed by lung adenocarcinoma (36.9%) and lung squamous cell carcinoma (28.1%). Three hundred seventy-six genes had significant correlations of their mutations with increased TMB in various cancers, including 11 genes (ARID1A, ARID1B, BRIP1, NOTCH2, NOTCH4, EPHA5, ROS1, FAT1, SPEN, NSD1,and PTPRT) with the characteristic of their mutations associated with a favorable response to immunotherapy. Based on the mutation profiles in three genes (ROS1, SPEN, and PTPRT), we defined the TMB prognostic score that could predict cancer survival prognosis in the immunotherapy setting but not in the non-immunotherapy setting. It suggests that the TMB prognostic score's ability to predict cancer prognosis is associated with the positive correlation between immunotherapy response and TMB. Nine cancer-associated pathways correlated positively with TMB in various cancers, including nucleotide excision repair, DNA replication, homologous recombination, base excision repair, mismatch repair, cell cycle, spliceosome, proteasome, and RNA degradation. In contrast, seven pathways correlated inversely with TMB in multiple cancers, including Wnt, Hedgehog, PI3K-AKT, MAPK, neurotrophin, axon guidance, and pathways in cancer. High-TMB cancers displayed higher levels of antitumor immune signatures and PD-L1 expression than low-TMB cancers in diverse cancers. The association between TMB and survival prognosis was positive in bladder, gastric, and endometrial cancers and negative in liver and head and neck cancers. TMB also showed significant associations with age, gender, height, weight, smoking, and race in certain cohorts. The molecular and clinical features significantly associated with TMB could be valuable predictors for TMB and immunotherapy response and therefore have potential clinical values for cancer management.

Real-World Data Validation of NAPOLI-1 Nomogram for the Prediction of Overall Survival in Metastatic Pancreatic Cancer

The nomogram derived from the pivotal phase III NAPOLI-1 study demonstrated a significant ability to predict median overall survival (OS) in gemcitabine-refractory metastatic pancreatic ductal adenocarcinoma (PDAC) treated with liposomal irinotecan plus fluorouracil and leucovorin (nal-IRI+5-FU/LV). However, the NAPOLI-1 nomogram has not been validated in a real-world setting and therefore the applicability of the NAPOLI-1 nomogram in daily practice remains unknown. This study aims to evaluate the NAPOLI-1 nomogram in a multicenter real-world cohort. The NAPOLI-1 nomogram was applied to a previously established cohort of metastatic PDAC patients treated with nal-IRI+5-FU/LV in nine participating centers in Taiwan. Patients were divided into three risk groups according to the NAPOLI-1 nomogram. The survival impact of relative dose intensity at 6 weeks (RDI at 6 weeks) in different risk groups was also investigated. Of the 473 included patients, the median OSs of patients classified as low (n = 156), medium (n = 186), and high (n = 131) risk were 10.9, 6.3, and 4.3 months, respectively (p < 0.0001). The survival impact of RDI at 6 weeks remained significant after stratification by risk groups, adjustment with Cox regression, inverse probability weighting, or propensity score matching. Our results support the usefulness of the NAPOLI-1 nomogram for risk stratification in gemcitabine-refractory metastatic PDAC treated with nal-IRI+5-FU/LV in daily practice. We further showed that the RDI at 6 weeks is an independent prognostic factor beyond the NAPOLI-1 nomogram.

Comparing machine learning approaches to incorporate time-varying covariates in predicting cancer survival time

The Cox proportional hazards model is commonly used in evaluating risk factors in cancer survival data. The model assumes an additive, linear relationship between the risk factors and the log hazard. However, this assumption may be too simplistic. Further, failure to take time-varying covariates into account, if present, may lower prediction accuracy. In this retrospective, population-based, prognostic study of data from patients diagnosed with cancer from 2008 to 2015 in Ontario, Canada, we applied machine learning-based time-to-event prediction methods and compared their predictive performance in two sets of analyses: (1) yearly-cohort-based time-invariant and (2) fully time-varying covariates analysis. Machine learning-based methods—gradient boosting model (gbm), random survival forest (rsf), elastic net (enet), lasso and ridge—were compared to the traditional Cox proportional hazards (coxph) model and the prior study which used the yearly-cohort-based time-invariant analysis. Using Harrell’s C index as our primary measure, we found that using both machine learning techniques and incorporating time-dependent covariates can improve predictive performance. Gradient boosting machine showed the best performance on test data in both time-invariant and time-varying covariates analysis.

Multicenter International Study of the Consensus Immunoscore for the Prediction of Relapse and Survival in Early-Stage Colon Cancer.

Background: The prognostic value of Immunoscore was evaluated in Stage II/III colon cancer (CC) patients, but it remains unclear in Stage I/II, and in early-stage subgroups at risk. An international Society for Immunotherapy of Cancer (SITC) study evaluated the pre-defined consensus Immunoscore in tumors from 1885 AJCC/UICC-TNM Stage I/II CC patients from Canada/USA (Cohort 1) and Europe/Asia (Cohort 2). METHODS: Digital-pathology is used to quantify the densities of CD3+ and CD8+ T-lymphocyte in the center of tumor (CT) and the invasive margin (IM). The time to recurrence (TTR) was the primary endpoint. Secondary endpoints were disease-free survival (DFS), overall survival (OS), prognosis in Stage I, Stage II, Stage II-high-risk, and microsatellite-stable (MSS) patients. RESULTS: High-Immunoscore presented with the lowest risk of recurrence in both cohorts. In Stage I/II, recurrence-free rates at 5 years were 78.4% (95%-CI, 74.4−82.6), 88.1% (95%-CI, 85.7−90.4), 93.4% (95%-CI, 91.1−95.8) in low, intermediate and high Immunoscore, respectively (HR (Hi vs. Lo) = 0.27 (95%-CI, 0.18−0.41); p < 0.0001). In Cox multivariable analysis, the association of Immunoscore to outcome was independent (TTR: HR (Hi vs. Lo) = 0.29, (95%-CI, 0.17−0.50); p < 0.0001) of the patient’s gender, T-stage, sidedness, and microsatellite instability-status (MSI). A significant association of Immunoscore with survival was found for Stage II, high-risk Stage II, T4N0 and MSS patients. The Immunoscore also showed significant association with TTR in Stage-I (HR (Hi vs. Lo) = 0.07 (95%-CI, 0.01−0.61); P = 0.016). The Immunoscore had the strongest (69.5%) contribution χ2 for influencing survival. Patients with a high Immunoscore had prolonged TTR in T4N0 tumors even for patients not receiving chemotherapy, and the Immunoscore remained the only significant parameter in multivariable analysis. CONCLUSION: In early CC, low Immunoscore reliably identifies patients at risk of relapse for whom a more intensive surveillance program or adjuvant treatment should be considered.

Using Single-Cell RNA Sequencing and MicroRNA Targeting Data to Improve Colorectal Cancer Survival Prediction.

Colorectal cancer has proven to be difficult to treat as it is the second leading cause of cancer death for both men and women worldwide. Recent work has shown the importance of microRNA (miRNA) in the progression and metastasis of colorectal cancer. Here, we develop a metric based on miRNA-gene target interactions, previously validated to be associated with colorectal cancer. We use this metric with a regularized Cox model to produce a small set of top-performing genes related to colon cancer. We show that using the miRNA metric and a Cox model led to a meaningful improvement in colon cancer survival prediction and correct patient risk stratification. We show that our approach outperforms existing methods and that the top genes identified by our process are implicated in NOTCH3 signaling and general metabolism pathways, which are essential to colon cancer progression.

Pancancer survival prediction using a deep learning architecture with multimodal representation and integration.

Use of multi-omics data carrying comprehensive signals about the disease is strongly desirable for understanding and predicting disease progression, cancer particularly as a serious disease with a high mortality rate. However, recent methods currently fail to effectively utilize the multi-omics data for cancer survival prediction and thus significantly limiting the accuracy of survival prediction using omics data. In this work, we constructed a deep learning model with multimodal representation and integration to predict the survival of patients using multi-omics data. We first developed an unsupervised learning part to extract high-level feature representations from omics data of different modalities. Then, we used an attention-based method to integrate feature representations, produced by the unsupervised learning part, into a single compact vector and finally we fed the vector into fully connected layers for survival prediction. We used multimodal data to train the model and predict pancancer survival, and the results show that using multimodal data can lead to higher prediction accuracy compared to using single modal data. Furthermore, we used the concordance index and the 5-fold cross-validation method for comparing our proposed method with current state-of-the-art methods and our results show that our model achieves better performance on the majority of cancer types in our testing datasets. https://github.com/ZhangqiJiang07/MultimodalSurvivalPrediction. Supplementary data are available at Bioinformatics online.

CAMR: cross-aligned multimodal representation learning for cancer survival prediction.

Accurately predicting cancer survival is crucial for helping clinicians to plan appropriate treatments, which largely improves the life quality of cancer patients and spares the related medical costs. Recent advances in survival prediction methods suggest that integrating complementary information from different modalities, e.g. histopathological images and genomic data, plays a key role in enhancing predictive performance. Despite promising results obtained by existing multimodal methods, the disparate and heterogeneous characteristics of multimodal data cause the so-called modality gap problem, which brings in dramatically diverse modality representations in feature space. Consequently, detrimental modality gaps make it difficult for comprehensive integration of multimodal information via representation learning and therefore pose a great challenge to further improvements of cancer survival prediction. To solve the above problems, we propose a novel method called cross-aligned multimodal representation learning (CAMR), which generates both modality-invariant and -specific representations for more accurate cancer survival prediction. Specifically, a cross-modality representation alignment learning network is introduced to reduce modality gaps by effectively learning modality-invariant representations in a common subspace, which is achieved by aligning the distributions of different modality representations through adversarial training. Besides, we adopt a cross-modality fusion module to fuse modality-invariant representations into a unified cross-modality representation for each patient. Meanwhile, CAMR learns modality-specific representations which complement modality-invariant representations and therefore provides a holistic view of the multimodal data for cancer survival prediction. Comprehensive experiment results demonstrate that CAMR can successfully narrow modality gaps and consistently yields better performance than other survival prediction methods using multimodal data. CAMR is freely available at https://github.com/wxq-ustc/CAMR. Supplementary data are available at Bioinformatics online.

Physician-reported ECOG-PS versus patient-reported (self) ECOG-PS: Which one is a better predictor of survival in cancer?

The aim of this study is to determine the discrepancy and agreement between Eastern Cooperative Oncology Group Performance Status (ECOG-PS) scores evaluated by doctors and patients and examine the factors that influence the performance evaluation of doctors and patients. This study is a prospective and descriptive case-control study. General, demographic, and oncologic data of the patients were collected. e-control study. General, demographic, and oncologic data of the patients were collected. Patient-Reported Outcomes Measurement Information System (PROMIS) anxiety and depression surveys were conducted to determine patients' levels of anxiety and depression, and at the same time, both the doctors and the patients were asked to determine their performance status. As a result, the discrepancy in performance evaluation between the patient and the doctor, the factors affecting this discrepancy, and its effect on progression and survival were examined. 277 patients were included. 146 (52.7%) were male, and 131 (47.3%) were female. The most common cancers were breast cancer (17.7%, n=49) and lung cancer (17.3%, n=48). It was found that the doctors' assessment of patient performance increased the progression risk by 3.1 times (HR: 3.080, 95% CI: 1,671-5,675) in ECOG-PS 2 compared to ECOG-PS 0 and by 5 times (HR: 4.980, 95% CI 1.405-17.646) in ECOG-PS 3 compared to ECOG-PS 0. Our study determined a weak agreement between the doctor and patient in terms of performance assessment. The most significant reason for this discrepancy was found to be due to the levels of depression or anxiety in patients. In conclusion, it was demonstrated that the performance evaluations determined by doctors are more accurate and meaningful in terms of progression and survival results compared to those determined by patients.

The Significance of Preoperative Computed Tomography Features in the Prediction of Overall Survival in Gastric Cancer: A Retrospective Analysis.

Computed tomography (CT) is a frequently used modality for staging in the preoperative evaluation of gastric cancer (GC). Our aim was to interpret the importance of preoperative CT features in predicting overall survival (OS) in patients operated for GC. One hundred and one patients with GC (33 women, 68 men; range of age: 29-82 years, median age: 61 years) who had abdominal CT prior to surgical resection were included in the study retrospectively. Two radiologists evaluated CT scans to record the longest dimension of the tumor, the localization of the lesion, the attenuation values of the tumor in the arterial and venous phases (Hounsfield units), invasion depth of the lesion (T stage), and the number of pathological lymph nodes (LNs) (N stage). Postoperative pathological results including resection (R0, R1), T stage, N stage, grade, and histopathological subtype were documented. All CT-provided results and clinicopathological features associated with OS were analyzed by univariate, multivariate, and receiver operator characteristic analysis. Multivariate analysis revealed that none of the CT features were associated with the OS. After resection, the survival ratio was poor for the R1 and high-grade groups than for the R0 and low-grade groups (p=0.001 and p=0.005, respectively). N stage and the longest dimension of the tumor on CT imaging truly estimated R1 resection status (AUC, 0.697; sensitivity, 63%; and specificity, 88%, and AUC, 0.734; sensitivity, 18%; and specificity, 76%, respectively). R1 resection status is associated with poor OS in GC. CT features, including the tumor's longest dimension and the number of pathological LNs, can predict R1 resection status.

Retracted: Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data.

[This retracts the article DOI: 10.1155/2022/1467070.].

An Efficient Ensemble Method Using K-Fold Cross Validation for the Early Detection of Benign and Malignant Breast Cancer

In comparison to all other malignancies, breast cancer is the most commonform of cancer, among women. Breast cancer prediction has been studied by a number of researchers, and is considered as a serious threat to women. Clinicians are finding it difficult to create a treatment approach that will help patients live longer, due to the lack of solid predictive models which predicts the outcome in early stages by analyzing history of patient’s data. Rates of this malignancy have been observed to rise, more with industrialization and urbanization, as well as with early detection facilities. It is still considerably more prevalent in very developed countries, but it is rapidly spreading to developing countries as well. The purpose of this work is to offer a report on the disease of breast cancer in which we used available technical breakthroughs to construct breast cancer survivability prediction models. The Machine Learning (ML) techniques, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT) Classifier, Random Forests (RF), and Logistic Regression (LR)are used as base Learners and their performance has been compared with the ensemble method, eXtreme Gradient Boosting(XGBoost). For performance comparison, we employed k-fold cross validation method to measure the unbiased estimate of these prediction models. The results indicated that XGBoost outperformed with an accuracy of 97.81% compared to other ML algorithms.

Joint learning sample similarity and correlation representation for cancer survival prediction

BackgroundAs a highly aggressive disease, cancer has been becoming the leading death cause around the world. Accurate prediction of the survival expectancy for cancer patients is significant, which can help clinicians make appropriate therapeutic schemes. With the high-throughput sequencing technology becoming more and more cost-effective, integrating multi-type genome-wide data has been a promising method in cancer survival prediction. Based on these genomic data, some data-integration methods for cancer survival prediction have been proposed. However, existing methods fail to simultaneously utilize feature information and structure information of multi-type genome-wide data.ResultsWe propose a Multi-type Data Joint Learning (MDJL) approach based on multi-type genome-wide data, which comprehensively exploits feature information and structure information. Specifically, MDJL exploits correlation representations between any two data types by cross-correlation calculation for learning discriminant features. Moreover, based on the learned multiple correlation representations, MDJL constructs sample similarity matrices for capturing global and local structures across different data types. With the learned discriminant representation matrix and fused similarity matrix, MDJL constructs graph convolutional network with Cox loss for survival prediction.ConclusionsExperimental results demonstrate that our approach substantially outperforms established integrative methods and is effective for cancer survival prediction.

A Gallbladder Cancer Survival Prediction Model Based on Multimodal Fusion Analysis

BackgroundGallbladder cancer is the sixth most common malignant gastrointestinal tumor. Radical surgery is currently the only effective treatment, but patient prognosis is poor, with a 5-year survival rate of only 5–10%. Establishing an effective survival prediction model for gallbladder cancer patients is crucial for disease status assessment, early intervention, and individualized treatment approaches. The existing gallbladder cancer survival prediction model uses clinical data—radiotherapy and chemotherapy, pathology, and surgical scope—but fails to utilize laboratory examination and imaging data, limiting its prediction accuracy and preventing sufficient treatment plan guidance.AimsThe aim of this work is to propose an accurate survival prediction model, based on the deep learning 3D-DenseNet network, integrated with multimodal medical data (enhanced CT imaging, laboratory test results, and data regarding systemic treatments).MethodsData were collected from 195 gallbladder cancer patients at two large tertiary hospitals in Shanghai. The 3D-DenseNet network extracted deep imaging features and constructed prognostic factors, from which a multimodal survival prediction model was established, based on the Cox regression model and incorporating patients’ laboratory test and systemic treatment data.ResultsThe model had a C-index of 0.787 in predicting patients’ survival rate. Moreover, the area under the curve (AUC) of predicting patients’ 1-, 3-, and 5-year survival rates reached 0.827, 0.865, and 0.926, respectively.ConclusionsCompared with the monomodal model based on deep imaging features and the tumor–node–metastasis (TNM) staging system—widely used in clinical practice—our model’s prediction accuracy was greatly improved, aiding the prognostic assessment of gallbladder cancer patients.

Post-treatment serum triglyceride: An effective biomarker for body fat mass and overall survival in esophageal squamous cell cancer patients treated with chemoradiotherapy.

Although lipids have been assessed for their possible roles in cancer survival prediction, studies on the association between serum triglyceride (TG) levels and the prognosis of esophageal squamous cell carcinoma (ESCC) patients are limited. This study aimed to evaluate whether serum TG is associated with outcomes in patients with ESCC and investigate any interaction between serum TG and clinical parameters, especially body fat mass. We conducted a prospective case study on patients diagnosed with ESCC between March 2012 and November 2018. We measured patients' serum TG levels before and after treatment. The association between serum TG and overall survival (OS) was evaluated using hazard ratios. We sought to determine a threshold point using optimal stratification. Survival analysis was performed using Kaplan-Meier curves and a Cox proportional hazards model. Of the 257 participants diagnosed with ESCC, 200 (77.8%) were men. Median follow-up time was 22.4 months (range 3.3-92.4 months). Using univariate Cox proportional hazard analysis and subsequent multivariate analysis, post-TG levels, Karnofsky performance scores, T stages, and chemotherapy cycles were shown to be independent prognostic factors for OS (p < 0.05). The post-TG cut-off point to best classify patients with respect to time to mortality was 1.47 mmol/L. A post-TG level of ≥ 1.47 mmol/L could independently predict a better OS (hazard ratio: 0.55, 95% confidence interval: 0.37-0.79). The associations were consistent across the subtypes of clinical parameters. Furthermore, the post-body mass index, post-subcutaneous adipose tissue area, post-visceral adipose tissue area, post-total adiposity tissue area, and post-total adipose density exhibited a strong positive association with post-TG levels. Post-TG levels were found to be a significant positive prognostic biomarker for body fat mass and OS in ESCC patients.

External Validation of the Individualized Prediction of Breast Cancer Survival (IPBS) Model for Estimating Survival after Surgery for Patients with Breast Cancer in Northern Thailand.

The individualized prediction of breast cancer survival (IPBS) model was recently developed. Although the model showed acceptable performance during derivation, its external performance remained unknown. This study aimed to validate the IPBS model using the data of breast cancer patients in Northern Thailand. An external validation study was conducted based on female patients with breast cancer who underwent surgery at Maharaj Nakorn Chiang Mai hospital from 2005 to 2015. Data on IPBS predictors were collected. The endpoints were 5-year overall survival (OS) and disease-free survival (DFS). The model performance was evaluated in terms of discrimination and calibration. Missing data were handled with multiple imputation. Of all 3581 eligible patients, 1868 were included. The 5-year OS and DFS were 85.2% and 81.9%. The IPBS model showed acceptable discrimination: C-statistics 0.706 to 0.728 for OS and 0.675 to 0.689 for DFS at 5 years. However, the IPBS model minimally overestimated both OS and DFS predictions. These overestimations were corrected after model recalibration. In this external validation study, the IPBS model exhibited good discriminative ability. Although it may provide minimal overestimation, recalibrating the model to the local context is a practical solution to improve the model calibration.