Short- and Long-term survival prediction in patients with acute type A aortic dissection undergoing open surgery

BACKGROUND

Acute Type A aortic dissection (ATAAD) is a life-threatening cardiovascular disease associated with high mortality rates, where surgical intervention remains the primary life-saving treatment. However, the mortality rate for ATAAD operations continues to be alarmingly high. To address this critical issue, our study aimed to assess the correlation between preoperative laboratory examination, clinical imaging data, and postoperative mortality in ATAAD patients. Additionally, we sought to establish a reliable prediction model for evaluating the risk of postoperative death.

METHODS

In this study, a total of 384 patients with acute type A aortic dissection (ATAAD) who were admitted to the emergency department for surgical treatment were included. Based on preoperative laboratory examination and clinical imaging data of ATAAD patients, logistic analysis was used to obtain independent risk factors for postoperative in-hospital death. The survival prediction model was based on cox regression analysis and displayed as a nomogram.

RESULTS

Logistic analysis identified several independent risk factors for postoperative in-hospital death, including Marfan syndrome, previous cardiac surgery history, previous renal dialysis history, direct bilirubin, serum phosphorus, D-dimer, white blood cell, multiple aortic ruptures and age. A survival prediction model based on cox regression analysis was established and presented as a nomogram. The model exhibited good discrimination and significantly improved the prediction of death risk in ATAAD patients.

CONCLUSIONS

In this study, we developed a novel survival prediction model for acute type A aortic dissection based on preoperative clinical features. The model demonstrated good discriminatory power and improved accuracy in predicting the risk of death in ATAAD patients undergoing open surgery.

Modelling-based joint embedding of histology and genomics using canonical correlation analysis for breast cancer survival prediction

Traditional approaches to predicting breast cancer patients' survival outcomes were based on clinical subgroups, the PAM50 genes, or the histological tissue's evaluation. With the growth of multi-modality datasets capturing diverse information (such as genomics, histology, radiology and clinical data) about the same cancer, information can be integrated using advanced tools and have improved survival prediction. These methods implicitly exploit the key observation that different modalities originate from the same cancer source and jointly provide a complete picture of the cancer. In this work, we investigate the benefits of explicitly modelling multi-modality data as originating from the same cancer under a probabilistic framework. Specifically, we consider histology and genomics as two modalities originating from the same breast cancer under a probabilistic graphical model (PGM). We construct maximum likelihood estimates of the PGM parameters based on canonical correlation analysis (CCA) and then infer the underlying properties of the cancer patient, such as survival. Equivalently, we construct CCA-based joint embeddings of the two modalities and input them to a learnable predictor. Real-world properties of sparsity and graph-structures are captured in the penalized variants of CCA (pCCA) and are better suited for cancer applications. For generating richer multi-dimensional embeddings with pCCA, we introduce two novel embedding schemes that encourage orthogonality to generate more informative embeddings. The efficacy of our proposed prediction pipeline is first demonstrated via low prediction errors of the hidden variable and the generation of informative embeddings on simulated data. When applied to breast cancer histology and RNA-sequencing expression data from The Cancer Genome Atlas (TCGA), our model can provide survival predictions with average concordance-indices of up to 68.32% along with interpretability. We also illustrate how the pCCA embeddings can be used for survival analysis through Kaplan-Meier curves.

Incorporation of the lepidic component as an additional pathological T descriptor for non-small cell lung cancer: Data from 3335 cases of lung adenocarcinoma

OBJECTIVES

The Lepidic Component (LP) identifies a subgroup with an excellent prognosis for lung adenocarcinoma (LUAD). Our research aimed to propose an improved pathological T (pT) stage for LUAD based on LP.

MATERIALS AND METHODS

Totally, 3335 surgical patients with pathological stage I LUAD were incorporated. Factors affecting survival were investigated by analyzing recurrence-free survival (RFS) and overall survival (OS) using the Kaplan-Meier method and Cox regression analyses. Subgroup analysis based on Lepidic Ratio (LR) was further evaluated. The net benefit from the modified pT category (pTm) was assessed using the Area Under the time-dependent Receiver Operating Curve (AUC), Harrell's Concordance Index (C-index), Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI).

RESULTS

The presence of LP (LP+) was identified in 1425 (42.7 %) patients, indicating a significantly better RFS (P < 0.001) and OS (P < 0.001) than those without LP, and similar results were reproduced in pT1a-pT2a subcategory (P < 0.050 for all). Multivariable Cox analysis revealed LP+ as an independent prognostic factor for both RFS (HR, 0.622; P < 0.001) and OS (HR, 0.710; P = 0.019). However, lepidic ratio (LR) was not independently associated with both RFS and OS for LP+ patients. The 5-year RFS and OS rates between T1a (LP-) and T1b (LP+), T1b (LP-) and T1c (LP+), and T1b (LP-) and T2a (LP+) were comparable (P > 0.050 for all). After modification, compared with current 8th edition pT stage system (pT8), pTm independently predicted RFS and OS, and AUCs, c-index, NRI, and IDI analysis all demonstrated pTm holds better discrimination performances than pT8 for LUAD prognosis.

CONCLUSION

LP can be an additional down-staged T descriptor for pathological stage I LUAD and improve the survival predictive performance of reclassification.

Development and internal validation of machine learning models for personalized survival predictions in spinal cord glioma patients

BACKGROUND CONTEXT

Numerous factors have been associated with the survival outcomes in patients with spinal cord gliomas (SCG). Recognizing these specific determinants is crucial, yet it is also vital to establish a reliable and precise prognostic model for estimating individual survival outcomes.

OBJECTIVE

The objectives of this study are twofold: first, to create an array of interpretable machine learning (ML) models developed for predicting survival outcomes among SCG patients; and second, to integrate these models into an easily navigable online calculator to showcase their prospective clinical applicability.

STUDY DESIGN

This was a retrospective, population-based cohort study aiming to predict the outcomes of interest, which were binary categorical variables, in SCG patients with ML models.

PATIENT SAMPLE

The National Cancer Database (NCDB) was utilized to identify adults aged 18 years or older who were diagnosed with histologically confirmed SCGs between 2010 and 2019.

OUTCOME MEASURES

The outcomes of interest were survival outcomes at three specific time points post-diagnosis: 1, 3, and 5 years. These outcomes were formed by combining the "Vital Status" and "Last Contact or Death (Months from Diagnosis)" variables. Model performance was evaluated visually and numerically. The visual evaluation utilized receiver operating characteristic (ROC) curves, precision-recall curves (PRCs), and calibration curves. The numerical evaluation involved metrics such as sensitivity, specificity, accuracy, area under the PRC (AUPRC), area under the ROC curve (AUROC), and Brier Score.

METHODS

We employed five ML algorithms-TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest-along with the Optuna library for hyperparameter optimization. The models that yielded the highest AUROC values were chosen for integration into the online calculator. To enhance the explicability of our models, we utilized SHapley Additive exPlanations (SHAP) for assessing the relative significance of predictor variables and incorporated partial dependence plots (PDPs) to delineate the influence of singular variables on the predictions made by the top performing models.

RESULTS

For the 1-year survival analysis, 4,913 patients [5.6% with 1-year mortality]; for the 3-year survival analysis, 4,027 patients (11.5% with 3-year mortality]; and for the 5-year survival analysis, 2,854 patients (20.4% with 5-year mortality) were included. The top models achieved AUROCs of 0.938 for 1-year mortality (TabPFN), 0.907 for 3-year mortality (LightGBM), and 0.902 for 5-year mortality (Random Forest). Global SHAP analyses across survival outcomes at different time points identified histology, tumor grade, age, surgery, radiotherapy, and tumor size as the most significant predictor variables for the top-performing models.

CONCLUSIONS

This study demonstrates ML techniques can develop highly accurate prognostic models for SCG patients with excellent discriminatory ability. The interactive online calculator provides a tool for assessment by physicians (https://huggingface.co/spaces/MSHS-Neurosurgery-Research/NCDB-SCG). Local interpretability informs prediction influences for a given individual. External validation across diverse datasets could further substantiate potential utility and generalizability. This robust, interpretable methodology aligns with the goals of precision medicine, establishing a foundation for continued research leveraging ML's predictive power to enhance patient counseling.

Mortality risk factors in patients receiving ECPR after cardiac arrest: Development and validation of a clinical prognostic prediction model

BACKGROUND

Previous studies have shown an increasing trend of extracorporeal cardiopulmonary resuscitation (ECPR) use in patients with cardiac arrest (CA). Although ECPR have been found to reduce mortality in patients with CA compared with conventional cardiopulmonary resuscitation (CCPR), the mortality remains high. This study was designed to identify the potential mortality risk factors for ECPR patients for further optimization of patient management and treatment selection.

METHODS

We conducted a prospective, multicentre study collecting 990 CA patients undergoing ECPR in 61 hospitals in China from January 2017 to May 2022 in CSECLS registry database. A clinical prediction model was developed using cox regression and validated with external data.

RESULTS

The data of 351 patients meeting the inclusion criteria before October 2021 was used to develop a prediction model and that of 68 patients after October 2021 for validation. Of the 351 patients with CA treated with ECPR, 227 (64.8%) patients died before hospital discharge. Multivariate analysis suggested that a medical history of cerebrovascular diseases, pulseless electrical activity (PEA)/asystole and higher Lactate (Lac) were risk factors for mortality while aged 45-60, higher pH and intra-aortic balloon pump (IABP) during ECPR have protective effects. Internal validation by bootstrap resampling was subsequently used to evaluate the stability of the model, showing moderate discrimination, especially in the early stage following ECPR, with a C statistic of 0.70 and adequate calibration with GOF chi-square = 10.4 (p = 0.50) for the entire cohort. Fair discrimination with c statistic of 0.65 and good calibration (GOF chi-square = 6.1, p = 0.809) in the external validation cohort demonstrating the model's ability to predict in-hospital death across a wide range of probabilities.

CONCLUSION

Risk factors have been identified among ECPR patients including a history of cerebrovascular diseases, higher Lac and presence of PEA or asystole. While factor such as age 45-60, higher pH and use of IABP have been found protective against in-hospital mortality. These factors can be used for risk prediction, thereby improving the management and treatment selection of patients for this resource-intensive therapy.

SurvIAE: Survival prediction with Interpretable Autoencoders from Diffuse Large B-Cells Lymphoma gene expression data

BACKGROUND

In Diffuse Large B-Cell Lymphoma (DLBCL), several methodologies are emerging to derive novel biomarkers to be incorporated in the risk assessment. We realized a pipeline that relies on autoencoders (AE) and Explainable Artificial Intelligence (XAI) to stratify prognosis and derive a gene-based signature.

METHODS

AE was exploited to learn an unsupervised representation of the gene expression (GE) from three publicly available datasets, each with its own technology. Multi-layer perceptron (MLP) was used to classify prognosis from latent representation. GE data were preprocessed as normalized, scaled, and standardized. Four different AE architectures (Large, Medium, Small and Extra Small) were compared to find the most suitable for GE data. The joint AE-MLP classified patients on six different outcomes: overall survival at 12, 36, 60 months and progression-free survival (PFS) at 12, 36, 60 months. XAI techniques were used to derive a gene-based signature aimed at refining the Revised International Prognostic Index (R-IPI) risk, which was validated in a fourth independent publicly available dataset. We named our tool SurvIAE: Survival prediction with Interpretable AE.

RESULTS

From the latent space of AEs, we observed that scaled and standardized data reduced the batch effect. SurvIAE models outperformed R-IPI with Matthews Correlation Coefficient up to 0.42 vs. 0.18 for the validation-set (PFS36) and to 0.30 vs. 0.19 for the test-set (PFS60). We selected the SurvIAE-Small-PFS36 as the best model and, from its gene signature, we stratified patients in three risk groups: R-IPI Poor patients with High levels of GAB1, R-IPI Poor patients with Low levels of GAB1 or R-IPI Good/Very Good patients with Low levels of GPR132, and R-IPI Good/Very Good patients with High levels of GPR132.

CONCLUSIONS

SurvIAE showed the potential to derive a gene signature with translational purpose in DLBCL. The pipeline was made publicly available and can be reused for other pathologies.

Analysis of the survival time of patients with heart failure with reduced ejection fraction: a Bayesian approach via a competing risk parametric model

PURPOSE

Heart failure (HF) is a widespread ailment and is a primary contributor to hospital admissions. The focus of this study was to identify factors affecting the extended-term survival of patients with HF, anticipate patient outcomes through cause-of-death analysis, and identify risk elements for preventive measures.

METHODS

A total of 435 HF patients were enrolled from the medical records of the Rajaie Cardiovascular Medical and Research Center, covering data collected between March and August 2018. After a five-year follow-up (July 2023), patient outcomes were assessed based on the cause of death. The survival analysis was performed with the AFT method with the Bayesian approach in the presence of competing risks.

RESULTS

Based on the results of the best model for HF-related mortality, age [time ratio = 0.98, confidence interval 95%: 0.96-0.99] and ADHF [TR = 0.11, 95% (CI): 0.01-0.44] were associated with a lower survival time. Chest pain in HF-related mortality [TR = 0.41, 95% (CI): 0.10-0.96] and in non-HF-related mortality [TR = 0.38, 95% (CI): 0.12-0.86] was associated with a lower survival time. The next significant variable in HF-related mortality was hyperlipidemia (yes): [TR = 0.34, 95% (CI): 0.13-0.64], and in non-HF-related mortality hyperlipidemia (yes): [TR = 0.60, 95% (CI): 0.37-0.90]. CAD [TR = 0.65, 95% (CI): 0.38-0.98], CKD [TR = 0.52, 95% (CI): 0.28-0.87], and AF [TR = 0.53, 95% (CI): 0.32-0.81] were other variables that were directly related to the reduction in survival time of patients with non-HF-related mortality.

CONCLUSION

The study identified distinct predictive factors for overall survival among patients with HF-related mortality or non-HF-related mortality. This differentiated approach based on the cause of death contributes to the estimation of patient survival time and provides valuable insights for clinical decision-making.

Prediction of survival of persons with advanced dementia using the advanced dementia prognostic tool: A 2-year prospective study

BACKGROUND

In this prospective study, we evaluated the usefulness of the advanced dementia prognostic tool (ADEPT) for estimating the 2-year survival of persons with advanced dementia (AD) in China.

METHODS

The study predicted the 2-year mortality of 115 persons with AD using the ADEPT score.

RESULTS

In total, 115 persons with AD were included in the study. Of these persons, 48 died. The mean ADEPT score was 13.0. The AUROC for the prediction of the 2-year mortality rate using the ADEPT score was 0.62. The optimal threshold of the ADEPT score was 11.2, which had an AUROC of 0.63, specificity of 41.8, and sensitivity of 83.3.

CONCLUSIONS

The ADEPT score based on a threshold of 11.2 may serve as a prognostic tool to determine the 2-year survival rate of persons with AD in Chongqing, China. However, further studies are needed to explore the nature of this relationship.

Sustained improved survival of patients with metastatic melanoma after the introduction of anti-PD-1-based therapies

BACKGROUND

The introduction of modern therapies improved the median survival of patients with metastatic melanoma (MM). Here, we determined the real-world impact of modern treatments on the long-term survival of MM.

METHODS

In a population-based study, we extracted all cases of MM diagnosed in four non-consecutive years marked by major changes in available 1st line treatments (2012, 2014, 2016, and 2018) from the Danish MM Database. Patients were grouped into "trial-like" and "trial-excluded" based on common trial eligibility criteria.

RESULTS

We observed a sustained improved survival of "trial-like" patients diagnosed in 2016 or in 2018, compared to 2012 or 2014, but no major differences in 2018 versus 2016. In contrast, while survival of "trial-excluded" patients in 2016 was better compared to 2014 and 2012, survival in 2018 was improved over all previous years. We then developed a prognostic model based on multivariable stratified Cox regression, to predict the survival of newly diagnosed MM patients. Internal validation showed excellent discrimination and calibration, with a time-area-under-the-curve above 0.79 at multiple time horizons, for up to four years after diagnosis.

CONCLUSIONS

The introduction of modern treatments such as anti-PD-1 has led to a sustained, improved survival of real-world patients with MM, regardless of their eligibility for clinical trials. We provide an updateable prognostic model that can be used to improve patient information. Overall, these data highlight a positive population-based impact of modern treatments and can help health technology assessment agencies worldwide to evaluate the appropriateness of drug pricing based on known cost-benefit data.

Deep-learning-based survival prediction of patients with lower limb melanoma

BACKGROUND

For the purpose to examine lower limb melanoma (LLM) and its long-term survival rate, we used data from the Surveillance, Epidemiology and End Results (SEER) database. To estimate the prognosis of LLM patients and assess its efficacy, we used a powerful deep learning and neural network approach called DeepSurv.

METHODS

We gathered data on those who had an LLM diagnosis between 2000 and 2019 from the SEER database. We divided the people into training and testing cohorts at a 7:3 ratio using a random selection technique. To assess the likelihood that LLM patients would survive, we compared the results of the DeepSurv model with those of the Cox proportional-hazards (CoxPH) model. Calibration curves, the time-dependent area under the receiver operating characteristic curve (AUC), and the concordance index (C-index) were all used to assess how accurate the predictions were.

RESULTS

In this study, a total of 26,243 LLM patients were enrolled, with 7873 serving as the testing cohort and 18,370 as the training cohort. Significant correlations with age, gender, AJCC stage, chemotherapy status, surgery status, regional lymph node removal and the survival outcomes of LLM patients were found by the CoxPH model. The CoxPH model's C-index was 0.766, which signifies a good degree of predicted accuracy. Additionally, we created the DeepSurv model using the training cohort data, which had a higher C-index of 0.852. In addition to calculating the 3-, 5-, and 8-year AUC values, the predictive performance of both models was evaluated. The equivalent AUC values for the CoxPH model were 0.795, 0.767, and 0.847, respectively. The DeepSurv model, in comparison, had better AUC values of 0.872, 0.858, and 0.847. In comparison to the CoxPH model, the DeepSurv model demonstrated greater prediction performance for LLM patients, as shown by the AUC values and the calibration curve.

CONCLUSION

We created the DeepSurv model using LLM patient data from the SEER database, which performed better than the CoxPH model in predicting the survival time of LLM patients.

Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma

PURPOSE

Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients.

METHODS

A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [18F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The DeepMTS-derived segmentation masks were leveraged to extract handcrafted radiomics features, which were also used for prognostic prediction (AutoRadio-Score). Finally, we developed a multi-task deep learning-based radiomic (MTDLR) nomogram by integrating DeepMTS-Score, AutoRadio-Score, and clinical data. Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were used to evaluate the discriminative ability of the proposed MTDLR nomogram. For patient stratification, the PFS rates of high- and low-risk patients were calculated using Kaplan-Meier method and compared with the observed PFS probability.

RESULTS

Our MTDLR nomogram achieved C-index of 0.818 (95% confidence interval (CI): 0.785-0.851), 0.752 (95% CI: 0.638-0.865), and 0.717 (95% CI: 0.641-0.793) and area under curve (AUC) of 0.859 (95% CI: 0.822-0.895), 0.769 (95% CI: 0.642-0.896), and 0.730 (95% CI: 0.634-0.826) in the training, internal validation, and external validation cohorts, which showed a statistically significant improvement over conventional radiomic nomograms. Our nomogram also divided patients into significantly different high- and low-risk groups.

CONCLUSION

Our study demonstrated that MTDLR nomogram can perform reliable and accurate prognostic prediction in LA-NPC patients, and also enabled better patient stratification, which could facilitate personalized treatment planning.

Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression prediction

OBJECTIVE

The rapid advancement of high-throughput technologies in the biomedical field has resulted in the accumulation of diverse omics data types, such as mRNA expression, DNA methylation, and microRNA expression, for studying various diseases. Integrating these multi-omics datasets enables a comprehensive understanding of the molecular basis of cancer and facilitates accurate prediction of disease progression.

METHODS

However, conventional approaches face challenges due to the dimensionality curse problem. This paper introduces a novel framework called Knowledge Distillation and Supervised Variational AutoEncoders utilizing View Correlation Discovery Network (KD-SVAE-VCDN) to address the integration of high-dimensional multi-omics data with limited common samples. Through our experimental evaluation, we demonstrate that the proposed KD-SVAE-VCDN architecture accurately predicts the progression of breast and kidney carcinoma by effectively classifying patients as long- or short-term survivors. Furthermore, our approach outperforms other state-of-the-art multi-omics integration models.

RESULTS

Our findings highlight the efficacy of the KD-SVAE-VCDN architecture in predicting the disease progression of breast and kidney carcinoma. By enabling the classification of patients based on survival outcomes, our model contributes to personalized and targeted treatments. The favorable performance of our approach in comparison to several existing models suggests its potential to contribute to the advancement of cancer understanding and management.

CONCLUSION

The development of a robust predictive model capable of accurately forecasting disease progression at the time of diagnosis holds immense promise for advancing personalized medicine. By leveraging multi-omics data integration, our proposed KD-SVAE-VCDN framework offers an effective solution to this challenge, paving the way for more precise and tailored treatment strategies for patients with different types of cancer.

ASARA, a prediction model based on Child-Pugh class in hepatocellular carcinoma patients undergoing transarterial chemoembolization

BACKGROUND

Due to the high heterogeneity among hepatocellular carcinoma (HCC) patients receiving transarterial chemoembolization (TACE), the prognosis of patients varies significantly. The decision-making on the initiation and/or repetition of TACE under different liver functions is a matter of concern in clinical practice. Thus, we aimed to develop a prediction model for TACE candidates using risk stratification based on varied liver function.

METHODS

A total of 222 unresectable HCC patients who underwent TACE as their only treatment were included in this study. Cox proportional hazards regression was performed to select the independent risk factors and establish a predictive model for the overall survival (OS). The model was validated in patients with different Child-Pugh class and compared to previous TACE scoring systems.

RESULTS

The five independent risk factors, including alpha-fetoprotein (AFP) level, maximal tumor size, the increase of albumin-bilirubin (ALBI) grade score, tumor response, and the increase of aspartate aminotransferase (AST), were used to build a prognostic model (ASARA). In the training and validation cohorts, the OS of patients with ASARA score ≤ 2 was significantly higher than that of patients with ASARA score > 2 (P < 0.001, P = 0.006, respectively). The ASARA model and its modified version "AS(ARA)" can effectively distinguish the OS (P < 0.001, P = 0.004) between patients with Child-Pugh class A and B, and the C-index was 0.687 and 0.706, respectively. For repeated TACE, the ASARA model was superior to Assessment for Retreatment with TACE (ART) and ALBI grade, maximal tumor size, AFP, and tumor response (ASAR) among Child-Pugh class A patients. For the first TACE, the performance of AS(ARA) was better than that of modified hepatoma arterial-embolization prognostic (mHAP), mHAP3, and ASA(R) models among Child-Pugh class B patients.

CONCLUSIONS

The ASARA scoring system is valuable in the decision-making of TACE repetition for HCC patients, especially Child-Pugh class A patients. The modified AS(ARA) can be used to screen the ideal candidate for TACE initiation in Child-Pugh class B patients with poor liver function.

Predicting lung cancer survival based on clinical data using machine learning: A review

Machine learning has gained popularity in predicting survival time in the medical field. This review examines studies utilizing machine learning and data-mining techniques to predict lung cancer survival using clinical data. A systematic literature review searched MEDLINE, Scopus, and Google Scholar databases, following reporting guidelines and using the COVIDENCE system. Studies published from 2000 to 2023 employing machine learning for lung cancer survival prediction were included. Risk of bias assessment used the prediction model risk of bias assessment tool. Thirty studies were reviewed, with 13 (43.3%) using the surveillance, epidemiology, and end results database. Missing data handling was addressed in 12 (40%) studies, primarily through data transformation and conversion. Feature selection algorithms were used in 19 (63.3%) studies, with age, sex, and N stage being the most chosen features. Random forest was the predominant machine learning model, used in 17 (56.6%) studies. While the number of lung cancer survival prediction studies is limited, the use of machine learning models based on clinical data has grown since 2012. Consideration of diverse patient cohorts and data pre-processing are crucial. Notably, most studies did not account for missing data, normalization, scaling, or standardized data, potentially introducing bias. Therefore, a comprehensive study on lung cancer survival prediction using clinical data is needed, addressing these challenges.

Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review

BACKGROUND

Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis.

OBJECTIVE

This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field.

METHODS

In this review, we searched Pubmed, Scopus, IEEEXplore, and Google Scholar online databases. The search terms included intervention terms (vision transformers) and the task (i.e., lung cancer, adenocarcinoma, etc.). Two reviewers independently screened the title and abstract to select relevant studies and performed the data extraction. A third reviewer was consulted to validate the inclusion and exclusion. Finally, the narrative approach was used to synthesize the data.

RESULTS

Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. Researchers have used the publicly available lung cancer datasets of the lung imaging database consortium and the cancer genome atlas. One study used a cluster of 48 GPUs, while other studies used one, two, or four GPUs.

CONCLUSION

It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/ .

Model-driven survival prediction after congenital heart surgery

OBJECTIVES

The objective of the study was to improve postoperative risk assessment in congenital heart surgery by developing a machine-learning model based on readily available peri- and postoperative parameters.

METHODS

Our bicentric retrospective data analysis from January 2014 to December 2019 of established risk parameters for dismal outcome was used to train and test a model to predict postoperative survival within the first 30 days. The Freiburg training data consisted of 780 procedures; the Heidelberg test data comprised 985 procedures. STAT mortality score, age, aortic cross-clamp time and postoperative lactate values over 24 h were considered.

RESULTS

Our model showed an area under the curve (AUC) of 94.86%, specificity of 89.48% and sensitivity of 85.00%, resulting in 3 false negatives and 99 false positives.The STAT mortality score and the aortic cross-clamp time each showed a statistically highly significant impact on postoperative mortality. Interestingly, a child's age was barely statistically significant. Postoperative lactate values indicated an increased mortality risk if they were either constantly at a high level or low during the first 8 h postoperatively with an increase afterwards.When considering parameters available before, at the end of and 24 h after surgery, the predictive power of the complete model achieved the highest AUC. This, compared to the already high predictive power alone (AUC 88.9%) of the STAT mortality score, translates to an error reduction of 53.5%.

CONCLUSIONS

Our model predicts postoperative survival after congenital heart surgery with great accuracy. Compared with preoperative risk assessments, our postoperative risk assessment reduces prediction error by half. Heightened awareness of high-risk patients should improve preventive measures and thus patient safety.

Geometric graph neural networks on multi-omics data to predict cancer survival outcomes

The advance of sequencing technologies has enabled a thorough molecular characterization of the genome in human cancers. To improve patient prognosis predictions and subsequent treatment strategies, it is imperative to develop advanced computational methods to analyze large-scale, high-dimensional genomic data. However, traditional machine learning methods face a challenge in handling the high-dimensional, low-sample size problem that is shown in most genomic data sets. To address this, our group has developed geometric network analysis techniques on multi-omics data in connection with prior biological knowledge derived from protein-protein interactions (PPIs) or pathways. Geometric features obtained from the genomic network, such as Ollivier-Ricci curvature and the invariant measure of the associated Markov chain, have been shown to be predictive of survival outcomes in various cancers. In this study, we propose a novel supervised deep learning method called geometric graph neural network (GGNN) that incorporates such geometric features into deep learning for enhanced predictive power and interpretability. More specifically, we utilize a state-of-the-art graph neural network with sparse connections between the hidden layers based on known biology of the PPI network and pathway information. Geometric features along with multi-omics data are then incorporated into the corresponding layers. The proposed approach utilizes a local-global principle in such a manner that highly predictive features are selected at the front layers and fed directly to the last layer for multivariable Cox proportional-hazards regression modeling. The method was applied to multi-omics data from the CoMMpass study of multiple myeloma and ten major cancers in The Cancer Genome Atlas (TCGA). In most experiments, our method showed superior predictive performance compared to other alternative methods.

Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study

BACKGROUNDS

The incidence of gastric cardiac cancer (GCC) has obviously increased recently with poor prognosis. It's necessary to compare GCC prognosis with other gastric sites carcinoma and set up an effective prognostic model based on a neural network to predict the survival of GCC patients.

METHODS

In the population-based cohort study, we first enrolled the clinical features from the Surveillance, Epidemiology and End Results (SEER) data (n = 31,397) as well as the public Chinese data from different hospitals (n = 1049). Then according to the diagnostic time, the SEER data were then divided into two cohorts, the train cohort (patients were diagnosed as GCC in 2010-2014, n = 4414) and the test cohort (diagnosed in 2015, n = 957). Age, sex, pathology, tumor, node, and metastasis (TNM) stage, tumor size, surgery or not, radiotherapy or not, chemotherapy or not and history of malignancy were chosen as the predictive clinical features. The train cohort was utilized to conduct the neural network-based prognostic predictive model which validated by itself and the test cohort. Area under the receiver operating characteristics curve (AUC) was used to evaluate model performance.

RESULTS

The prognosis of GCC patients in SEER database was worse than that of non GCC (NGCC) patients, while it was not worse in the Chinese data. The total of 5371 patients were used to conduct the model, following inclusion and exclusion criteria. Neural network-based prognostic predictive model had a satisfactory performance for GCC overall survival (OS) prediction, which owned 0.7431 AUC in the train cohort (95% confidence intervals, CI, 0.7423-0.7439) and 0.7419 in the test cohort (95% CI, 0.7411-0.7428).

CONCLUSIONS

GCC patients indeed have different survival time compared with non GCC patients. And the neural network-based prognostic predictive tool developed in this study is a novel and promising software for the clinical outcome analysis of GCC patients.

Mortality during or shortly after Curative-Intent Radio-(Chemo-)Therapy over the last decade at a large comprehensive cancer center

Background and Introduction

Definitive surgical, oncological and radio-oncological treatment may result in significant morbidity and acute mortality. Mortality during or shortly after treatment in patients undergoing curative radio-(chemo)-therapy has not been studied systematically. We reviewed all curative radio-(chemo-)therapies at a large comprehensive cancer center over the last decade.

Materials and Methods

The institutional record was screened for patients who received curative-intent radio-(chemo-)therapy and deceased during or within 30 days after radiotherapy. Curative therapy was defined as prescribed dosage of EQD2 ≥ 50 Gy for radiotherapy alone and EQD2 ≥ 40 Gy for radiochemotherapies. Data on demographics, disease and treatment were assembled and assessed.

Results

Of 15,255 radiotherapy courses delivered at our center, 8,515 (56%) were performed with curative-intent. During or within 30 days after radio-(chemo-)therapy, 78 patients died (0.9% of all curative-intent courses). Median age of the deceased patients was 70 (IQR, 62-78) years, and 36% (28/78) were female. Median pre-therapeutic ECOG-PS was 1 (IQR, 0-2) and Charlson-Comorbidity-Index was 3+ (IQR, 2-3+). The most common primary malignancies were head and neck cancer (33/78; 42%) and central nervous system tumors (13/78; 17%). Peritherapeutic mortality varied by primary tumor, with the highest prevalence observed in head and neck and gastrointestinal cancer patients with 2.9% (33/1,144) and 2.4% (8/332), respectively. Among patients with known cause of death (34/78; 44%), tumor progression (12/34; 35%) and pulmonary complications/causes (11/34; 35%) were most common. On multivariable regression analysis, a worse ECOG-PS was associated with a relatively earlier peri-radiotherapeutic death (p = 0.014).

Conclusion

Mortality during or within 30 days of curative-intent radio-(chemo-)therapy was low, yet highest for head and neck (2.9%) and gastrointestinal tumor (2.4%) patients. Reasons for these findings include rapid tumor progression in some cancers, good patient selection, with ECOG-PS being most useful and predictive for avoiding early mortality. Future research should help refine predictors for peri-RT mortality.

Radiomics for therapy-specific head and neck squamous cell carcinoma survival prognostication (part I)

BACKGROUND

Treatment plans for squamous cell carcinoma of the head and neck (SCCHN) are individually decided in tumor board meetings but some treatment decision-steps lack objective prognostic estimates. Our purpose was to explore the potential of radiomics for SCCHN therapy-specific survival prognostication and to increase the models' interpretability by ranking the features based on their predictive importance.

METHODS

We included 157 SCCHN patients (male, 119; female, 38; mean age, 64.39 ± 10.71 years) with baseline head and neck CT between 09/2014 and 08/2020 in this retrospective study. Patients were stratified according to their treatment. Using independent training and test datasets with cross-validation and 100 iterations, we identified, ranked and inter-correlated prognostic signatures using elastic net (EN) and random survival forest (RSF). We benchmarked the models against clinical parameters. Inter-reader variation was analyzed using intraclass-correlation coefficients (ICC).

RESULTS

EN and RSF achieved top prognostication performances of AUC = 0.795 (95% CI 0.767-0.822) and AUC = 0.811 (95% CI 0.782-0.839). RSF prognostication slightly outperformed the EN for the complete (ΔAUC 0.035, p = 0.002) and radiochemotherapy (ΔAUC 0.092, p < 0.001) cohort. RSF was superior to most clinical benchmarking (p ≤ 0.006). The inter-reader correlation was moderate or high for all features classes (ICC ≥ 0.77 (± 0.19)). Shape features had the highest prognostic importance, followed by texture features.

CONCLUSIONS

EN and RSF built on radiomics features may be used for survival prognostication. The prognostically leading features may vary between treatment subgroups. This warrants further validation to potentially aid clinical treatment decision making in the future.

CoADS: Cross attention based dual-space graph network for survival prediction of lung cancer using whole slide images

BACKGROUND AND OBJECTIVE

Accurate overall survival (OS) prediction for lung cancer patients is of great significance, which can help classify patients into different risk groups to benefit from personalized treatment. Histopathology slides are considered the gold standard for cancer diagnosis and prognosis, and many algorithms have been proposed to predict the OS risk. Most methods rely on selecting key patches or morphological phenotypes from whole slide images (WSIs). However, OS prediction using the existing methods exhibits limited accuracy and remains challenging.

METHODS

In this paper, we propose a novel cross-attention-based dual-space graph convolutional neural network model (CoADS). To facilitate the improvement of survival prediction, we fully take into account the heterogeneity of tumor sections from different perspectives. CoADS utilizes the information from both physical and latent spaces. With the guidance of cross-attention, both the spatial proximity in physical space and the feature similarity in latent space between different patches from WSIs are integrated effectively.

RESULTS

We evaluated our approach on two large lung cancer datasets of 1044 patients. The extensive experimental results demonstrated that the proposed model outperforms state-of-the-art methods with the highest concordance index.

CONCLUSIONS

The qualitative and quantitative results show that the proposed method is more powerful for identifying the pathology features associated with prognosis. Furthermore, the proposed framework can be extended to other pathological images for predicting OS or other prognosis indicators, and thus delivering individualized treatment.

Integration of IDPC Clustering Analysis and Interpretable Machine Learning for Survival Risk Prediction of Patients with ESCC

Precise forecasting of survival risk plays a pivotal role in comprehending and predicting the prognosis of patients afflicted with esophageal squamous cell carcinoma (ESCC). The existing methods have the problems of insufficient fitting ability and poor interpretability. To address this issue, this work proposes a novel interpretable survival risk prediction method for ESCC patients based on extreme gradient boosting improved by whale optimization algorithm (WOA-XGBoost) and shapley additive explanations (SHAP). Given the imbalanced nature of the data set, the adaptive synthetic sampling (ADASYN) is first used to generate the samples with high survival risk. Then, an improved clustering by fast search and find of density peaks (IDPC) algorithm based on cosine distance and K nearest neighbors is used to cluster the patients. Next, the prediction model for each cluster is obtained by WOA-XGBoost and the constructed model is visualized with SHAP to uncover the factors hidden in the structured model and improve the interpretability of the black-box model. Finally, the effectiveness of the proposed scheme is demonstrated by analyzing the data collected from the First Affiliated Hospital of Zhengzhou University. The results of the analysis reveal that the proposed methodology exhibits superior performance, as indicated by the area under the receiver operating characteristic curve (AUROC) of 0.918 and accuracy of 0.881.

Survival prediction of heart failure patients using motion-based analysis method

BACKGROUND AND OBJECTIVE

Survival prediction of heart failure patients is critical to improve the prognostic management of the cardiovascular disease. The existing survival prediction methods focus on the clinical information while lacking the cardiac motion information. we propose a motion-based analysis method to predict the survival risk of heart failure patients for aiding clinical diagnosis and treatment.

METHODS

We propose a motion-based analysis method for survival prediction of heart failure patients. First, our method proposes the hierarchical spatial-temporal structure to capture the myocardial border. It promotes the model discrimination on border features. Second, our method explores the dense optical flow structure to capture motion fields. It improves the tracking capability on cardiac images. The cardiac motion information is obtained by fusing boundary information and motion fields of cardiac images. Finally, our method proposes the multi-modality deep-cox structure to predict the survival risk of heart failure patients. It improves the survival probability of heart failure patients.

RESULTS

The motion-based analysis method is confirmed to be able to improve the survival prediction of heart failure patients. The precision, recall, F1-score, and C-index are 0.8519, 0.8333, 0.8425, and 0.8478, respectively, which is superior to other state-of-the-art methods.

CONCLUSIONS

The experimental results show that the proposed model can effectively predict survival risk of heart failure patients. It facilitates the application of robust clinical treatment strategies.

Development and validation of a deep learning survival model for cervical adenocarcinoma patients

BACKGROUND

The aim was to develop a personalized survival prediction deep learning model for cervical adenocarcinoma patients and process personalized survival prediction.

METHODS

A total of 2501 cervical adenocarcinoma patients from the surveillance, epidemiology and end results database and 220 patients from Qilu hospital were enrolled in this study. We created our deep learning (DL) model to manipulate the data and evaluated its performance against four other competitive models. We tried to demonstrate a new grouping system oriented by survival outcomes and process personalized survival prediction by using our DL model.

RESULTS

The DL model reached 0.878 c-index and 0.09 Brier score in the test set, which was better than the other four models. In the external test set, our model achieved a 0.80 c-index and 0.13 Brier score. Thus, we developed prognosis-oriented risk grouping for patients according to risk scores computed by our DL model. Notable differences among groupings were observed. In addition, a personalized survival prediction system based on our risk-scoring grouping was developed.

CONCLUSIONS

We developed a deep neural network model for cervical adenocarcinoma patients. The performance of this model proved to be superior to other models. The results of external validation supported the possibility that the model can be used in clinical work. Finally, our survival grouping and personalized prediction system provided more accurate prognostic information for patients than traditional FIGO stages.

GraphLSurv: A scalable survival prediction network with adaptive and sparse structure learning for histopathological whole-slide images

BACKGROUND AND OBJECTIVE

Predicting patients' survival from gigapixel Whole-Slide Images (WSIs) has always been a challenging task. To learn effective WSI representations for survival prediction, existing deep learning methods have explored utilizing graphs to describe the complex structure inner WSIs, where graph node is respective to WSI patch. However, these graphs are often densely-connected or static, leading to some redundant or missing patch correlations. Moreover, these methods cannot be directly scaled to the very-large WSI with more than 10,000 patches. To address these, this paper proposes a scalable graph convolution network, GraphLSurv, which can efficiently learn adaptive and sparse structures to better characterize WSIs for survival prediction.

METHODS

GraphLSurv has three highlights in methodology: (1) it generates adaptive and sparse structures for patches so that latent patch correlations could be captured and adjusted dynamically according to prediction tasks; (2) based on the generated structure and a given graph, GraphLSurv further aggregates local microenvironmental cues into a non-local embedding using the proposed hybrid message passing network; (3) to make this network suitable for very large-scale graphs, it adopts an anchor-based technique to reduce theorical computation complexity.

RESULTS

The experiments on 2268 WSIs show that GraphLSurv achieves a concordance-index of 0.66132 and 0.68348, with an improvement of 3.79% and 3.41% compared to existing methods, on NLST and TCGA-BRCA, respectively.

CONCLUSIONS

GraphLSurv could often perform better than previous methods, which suggests that GraphLSurv could provide an important and effective means for WSI survival prediction. Moreover, this work empirically shows that adaptive and sparse structures could be more suitable than static or dense ones for modeling WSIs.

A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods

The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 47 independent breast cancer gene signatures, benchmarked on survival information from clinical data in the NKI dataset. Here, relying on the stability of cell line data and associative prior knowledge, we first demonstrate through Principal Component Analysis (PCA) that SPS prioritizes survival information over secondary subtype information, surpassing both PAM50 and Boruta, an artificial intelligence-based feature-selection algorithm, in this regard. We can also extract higher resolution 'progression' information using SPS, dividing survival outcomes into several clinically relevant stages ('good', 'intermediate', and 'bad) based on different quadrants of the PCA scatterplot. Furthermore, by transferring these 'progression' annotations onto independent clinical datasets, we demonstrate the generalisability of our method on actual patient data. Finally, via the characteristic genetic profiles of each quadrant/stage, we identified efficacious drugs using their gene reversal scores that can shift signatures across quadrants/stages, in a process known as gene signature reversal. This confirms the power of meta-analytical approaches for gene signature inference in breast cancer, as well as the clinical benefit in translating these inferences onto real-world patient data for more targeted therapies.

Predicting survival after radiosurgery in patients with lung cancer brain metastases using deep learning of radiomics and EGFR status

The early prediction of overall survival (OS) in patients with lung cancer brain metastases (BMs) after Gamma Knife radiosurgery (GKRS) can facilitate patient management and outcome improvement. However, the disease progression is influenced by multiple factors, such as patient characteristics and treatment strategies, and hence satisfactory performance of OS prediction remains challenging. Accordingly, we proposed a deep learning approach based on comprehensive predictors, including clinical, imaging, and genetic information, to accomplish reliable and personalized OS prediction in patients with BMs after receiving GKRS. Overall 1793 radiomic features extracted from pre-GKRS magnetic resonance images (MRI), clinical information, and epidermal growth factor receptor (EGFR) mutation status were retrospectively collected from 237 BM patients who underwent GKRS. DeepSurv, a multi-layer perceptron model, with 4 different aggregation methods of radiomics was applied to predict personalized survival curves and survival status at 3, 6, 12, and 24 months. The model combining clinical features, EGFR status, and radiomics from the largest BM showed the best prediction performance with concordance index of 0.75 and achieved areas under the curve of 0.82, 0.80, 0.84, and 0.92 for predicting survival status at 3, 6, 12, and 24 months, respectively. The DeepSurv model showed a significant improvement (p < 0.001) in concordance index compared to the validated lung cancer BM prognostic molecular markers. Furthermore, the model provided a novel estimate of the risk-of-death period for patients. The personalized survival curves generated by the DeepSurv model effectively predicted the risk-of-death period which could facilitate personalized management of patients with lung cancer BMs.

Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients

BACKGROUND

Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of lung cancers are non-small cell lung cancer (NSCLC), accounting for approximately 85% of all lung cancer types. The Cox proportional hazards model (CPH), which is the standard method for survival analysis, has several limitations. The purpose of our study was to improve survival prediction in patients with NSCLC by incorporating prognostic information from F-18 fluorodeoxyglucose positron emission tomography (FDG PET) images into a traditional survival prediction model using clinical data.

RESULTS

The multimodal deep learning model showed the best performance, with a C-index and mean absolute error of 0.756 and 399 days under a five-fold cross-validation, respectively, followed by ResNet3D for PET (0.749 and 405 days) and CPH for clinical data (0.747 and 583 days).

CONCLUSION

The proposed deep learning-based integrative model combining the two modalities improved the survival prediction in patients with NSCLC.

Tumor micronecrosis predicts poor prognosis of patients with hepatocellular carcinoma after liver transplantation

BACKGROUND

Tumor micronecrosis is a histopathological feature predicting poor prognosis in patients with hepatocellular carcinoma (HCC) who underwent liver resection. However, the role of tumor micronecrosis in liver transplantation remains unclear.

METHODS

We retrospectively reviewed patients with HCC who underwent liver transplantation between January 2015 and December 2021 at our center. We then classified them into micronecrosis(-) and micronecrosis(+) groups and compared their recurrence-free survival (RFS) and overall survival (OS). We identified independent prognostic factors using Cox regression analysis and calculated the area under the receiver operating characteristic curve (AUC) to evaluate the predictive value of RFS for patients with HCC after liver transplantation.

RESULTS

A total of 370 cases with evaluable histological sections were included. Patients of the micronecrosis(+) group had a significantly shorter RFS than those of the micronecrosis(-) group (P = 0.037). Shorter RFS and OS were observed in micronecrosis(+) patients without bridging treatments before liver transplantation (P = 0.002 and P = 0.007), while no differences were detected in those with preoperative antitumor therapies that could cause iatrogenic tumor necrosis. Tumor micronecrosis improved the AUC of Milan criteria (0.77-0.79), the model for end-stage liver disease score (0.70-0.76), and serum alpha-fetoprotein (0.63-0.71) for the prediction of prognosis after liver transplantation.

CONCLUSION

Patients with HCC with tumor micronecrosis suffer from a worse prognosis than those without this feature. Tumor micronecrosis can help predict RFS after liver transplantation. Therefore, patients with HCC with tumor micronecrosis should be treated with adjuvant therapy and closely followed after liver transplantation.

CLINICAL TRIALS REGISTRATION

Not Applicable.

Construction of a predictive model for clinical survival in male patients with non-metastatic rectal adenocarcinoma

BACKGROUND

No clinical prediction model is available for non-metastatic rectal adenocarcinoma in males. Based on demographic and clinicopathological characteristics, we constructed a survival prediction model for the study population.

METHODS

At a ratio of 7:3, 3450 eligible patients were divided into training and validation sets. Optimal cutoff values were calculated using X-tile software. Cox proportional hazards regression was used to find prognostic factors for cancer-specific survival (CSS) and overall survival (OS). Corresponding nomogram prognostic models were also constructed based on predictors.The validity, discriminative ability, predictability, and clinical usefulness of the model were analyzed and assessed.

RESULTS

We identified predictors of survival in the target population and successfully constructed nomograms. In the nomogram prediction model for OS and CSS, the C-index was 0.724 and 0.735, respectively, for the training group and 0.754 and 0.760, respectively, for the validation group. In the validation group, the area under the curve (AUC) of the receiver operating characteristic curve for OS and CSS nomograms was 0.768 and 0.769, respectively, for the 3-year survival rate and 0.755 and 0.747, respectively, for the 5-year survival rate. Kaplan-Meier Survival Curves showed excellent risk discrimination performance of the nomogram (P < 0.05) Calibration curves, time-dependent AUC and decision curve analysis showed that the prediction model constructed in this study had excellent clinical prediction and decision ability and performed better than the TNM staging system.

CONCLUSION

Our nomogram is helpful to evaluate the prognosis of non-metastatic male patients with rectal adenocarcinoma and has guiding significance for clinical treatment.

Application of machine learning techniques for predicting survival in ovarian cancer

BACKGROUND

Ovarian cancer is the fifth leading cause of mortality among women in the United States. Ovarian cancer is also known as forgotten cancer or silent disease. The survival of ovarian cancer patients depends on several factors, including the treatment process and the prognosis.

METHODS

The ovarian cancer patients' dataset is compiled from the Surveillance, Epidemiology, and End Results (SEER) database. With the help of a clinician, the dataset is curated, and the most relevant features are selected. Pearson's second coefficient of skewness test is used to evaluate the skewness of the dataset. Pearson correlation coefficient is also used to investigate the associations between features. Statistical test is utilized to evaluate the significance of the features. Six Machine Learning (ML) models, including K-Nearest Neighbors , Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), are implemented for survival prediction in both classification and regression approaches. An interpretable method, Shapley Additive Explanations (SHAP), is applied to clarify the decision-making process and determine the importance of each feature in prediction. Additionally, DTs of the RF model are displayed to show how the model predicts the survival intervals.

RESULTS

Our results show that RF (Accuracy = 88.72%, AUC = 82.38%) and XGBoost (Root Mean Squad Error (RMSE)) = 20.61%, R² = 0.4667) have the best performance for classification and regression approaches, respectively. Furthermore, using the SHAP method along with extracted DTs of the RF model, the most important features in the dataset are identified. Histologic type ICD-O-3, chemotherapy recode, year of diagnosis, age at diagnosis, tumor stage, and grade are the most important determinant factors in survival prediction.

CONCLUSION

To the best of our knowledge, our study is the first study that develops various ML models to predict ovarian cancer patients' survival on the SEER database in both classification and regression approaches. These ML algorithms also achieve more accurate results and outperform statistical methods. Furthermore, our study is the first study to use the SHAP method to increase confidence and transparency of the proposed models' prediction for clinicians. Moreover, our developed models, as an automated auxiliary tool, can help clinicians to have a better understanding of the estimated survival as well as important features that affect survival.

A prognostic nomogram to predict survival in elderly patients with small-cell lung cancer: a large population-based cohort study and external validation

BACKGROUND

Age is an independent prognostic factor for small cell lung cancer (SCLC). We aimed to construct a nomogram survival prediction for elderly SCLC patients based on the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

A total of 2851 elderly SCLC patients from the SEER database were selected as a primary cohort, which were randomly divided into a training cohort and an internal validation cohort. Additionally, 512 patients from two institutions in China were identified as an external validation cohort. We used univariate and multivariate to determine the independent prognostic factors and establish a nomogram to predict survival. The value of the nomogram was evaluated by calibration plots, concordance index (C-index) and decision curve analysis (DCA).

RESULTS

Ten independent prognostic factors were determined and integrated into the nomogram. Calibration plots showed an ideal agreement between the nomogram predicted and actual observed probability of survival. The C-indexes of the training and validation groups for cancer-specific survival (CSS) (0.757 and 0.756, respectively) based on the nomogram were higher than those of the TNM staging system (0.631 and 0.638, respectively). Improved AUC value and DCA were also obtained in comparison with the TNM model. The risk stratification system can significantly distinguish individuals with different survival risks.

CONCLUSION

We constructed and externally validated a nomogram to predict survival for elderly patients with SCLC. Our novel nomogram outperforms the traditional TNM staging system and provides more accurate prediction for the prognosis of elderly SCLC patients.

Validation of Traditional Prognosis Scoring Systems and Skeletal Oncology Research Group Nomogram for Predicting Survival of Spinal Metastasis Patients Undergoing Surgery

BACKGROUND

Many scoring systems that predict overall patient survival are based on clinical parameters and primary tumor type. To date, no consensus exists regarding which scoring system has the greatest predictive survival accuracy, especially when applied to specific primary tumors. Additionally, such scores usually fail to include modern treatment modalities, which influence patient survival. This study aimed to evaluate both the overall predictive accuracy of such scoring systems and the predictive accuracy based on the primary tumor.

METHODS

A retrospective review on spinal metastasis patients who were aged more than 18 years and underwent surgical treatment was conducted between October 2008 and August 2018. Patients were scored based on data before the time of surgery. A survival probability was calculated for each patient using the given scoring systems. The predictive ability of each scoring system was assessed using receiver operating characteristic analysis at postoperative time points; area under the curve was then calculated to quantify predictive accuracy.

RESULTS

A total of 186 patients were included in this analysis: 101 (54.3%) were men and the mean age was 57.1 years. Primary tumors were lung in 37 (20%), breast in 26 (14%), prostate in 20 (10.8%), hematologic malignancy in 18 (9.7%), thyroid in 10 (5.4%), gastrointestinal tumor in 25 (13.4%), and others in 40 (21.5%). The primary tumor was unidentified in 10 patients (5.3%). The overall survival was 201 days. For survival prediction, the Skeletal Oncology Research Group (SORG) nomogram showed the highest performance when compared to other prognosis scores in all tumor metastasis but a lower performance to predict survival with lung cancer. The revised Katagiri score demonstrated acceptable performance to predict death for breast cancer metastasis. The Tomita and revised Tokuhashi scores revealed acceptable performance in lung cancer metastasis. The New England Spinal Metastasis Score showed acceptable performance for predicting death in prostate cancer metastasis. SORG nomogram demonstrated acceptable performance for predicting death in hematologic malignancy metastasis at all time points.

CONCLUSIONS

The results of this study demonstrated inconsistent predictive performance among the prediction models for the specific primary tumor types. The SORG nomogram revealed the highest predictive performance when compared to previous survival prediction models.

SurvivalCNN: A deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables

Radiological images have shown promising effects in patient prognostication. Deep learning provides a powerful approach for in-depth analysis of imaging data and integration of multi-modal data for modeling. In this work, we propose SurvivalCNN, a deep learning structure for cancer patient survival prediction using CT imaging data and non-imaging clinical data. In SurvivalCNN, a supervised convolutional neural network is designed to extract volumetric image features, and radiomics features are also integrated to provide potentially different imaging information. Within SurvivalCNN, a novel multi-thread multi-layer perceptron module, namely, SurvivalMLP, is proposed to perform survival prediction from censored survival data. We evaluate the proposed SurvivalCNN framework on a large clinical dataset of 1061 gastric cancer patients for both overall survival (OS) and progression-free survival (PFS) prediction. We compare SurvivalCNN to three different modeling methods and examine the effects of various sets of data/features when used individually or in combination. With five-fold cross validation, our experimental results show that SurvivalCNN achieves averaged concordance index 0.849 and 0.783 for predicting OS and PFS, respectively, outperforming the compared state-of-the-art methods and the clinical model. After future validation, the proposed SurvivalCNN model may serve as a clinical tool to improve gastric cancer patient survival estimation and prognosis analysis.

Machine Learning Predict Survivals of Spinal and Pelvic Ewing's Sarcoma with the SEER Database

STUDY DESIGN

Retrospective Cohort Study.

OBJECTIVES

This study aimed to develop survival prediction models for spinal Ewing's sarcoma (EWS) based on machine learning (ML).

METHODS

We extracted the SEER registry's clinical data of EWS diagnosed between 1975 and 2016. Three feature selection methods extracted clinical features. Four ML algorithms (Cox, random survival forest (RSF), CoxBoost, DeepCox) were trained to predict the overall survival (OS) and cancer-specific survival (CSS) of spinal EWS. The concordance index (C-index), integrated Brier score (IBS) and mean area under the curves (AUC) were used to assess the prediction performance of different ML models. The top initial ML models with best performance from each evaluation index (C-index, IBS and mean AUC) were finally stacked to ensemble models which were compared with the traditional TNM stage model by 3-/5-/10-year Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA).

RESULTS

A total of 741 patients with spinal EWS were identified. C-index, IBS and mean AUC for the final ensemble ML model in predicting OS were .693/0.158/0.829 during independent testing, while .719/0.171/0.819 in predicting CSS. The ensemble ML model also achieved an AUC of .705/0.747/0.851 for predicting 3-/5-/10-year OS during independent testing, while .734/0.779/0.830 for predicting 3-/5-/10-year CSS, both of which outperformed the traditional TNM stage. DCA curves also showed the advantages of the ensemble models over the traditional TNM stage.

CONCLUSION

ML was an effective and promising technique in predicting survival of spinal EWS, and the ensemble models were superior to the traditional TNM stage model.

SORG algorithm to predict 3- and 12-month survival in metastatic spinal disease: a cross-sectional population-based retrospective study

PURPOSE

In this study, we wished to compare statistically the novel SORG algorithm in predicting survival in spine metastatic disease versus currently used methods.

METHODS

We recruited 40 patients with spinal metastatic disease who were operated at Geneva University Hospitals by the Neurosurgery or Orthopedic teams between the years of 2015 and 2020. We did an ROC analysis in order to determine the accuracy of the SORG ML algorithm and nomogram versus the Tokuhashi original and revised scores.

RESULTS

The analysis of data of our independent cohort shows a clear advantage in terms of predictive ability of the SORG ML algorithm and nomogram in comparison with the Tokuhashi scores. The SORG ML had an AUC of 0.87 for 90 days and 0.85 for 1 year. The SORG nomogram showed a predictive ability at 90 days and 1 year with AUCs of 0.87 and 0.76 respectively. These results showed excellent discriminative ability as compared with the Tokuhashi original score which achieved AUCs of 0.70 and 0.69 and the Tokuhashi revised score which had AUCs of 0.65 and 0.71 for 3 months and 1 year respectively.

CONCLUSION

The predictive ability of the SORG ML algorithm and nomogram was superior to currently used preoperative survival estimation scores for spinal metastatic disease.

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer

BACKGROUND

Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world - lung cancer, their interrelations are not well understood. Digital pathology data provides a unique insight into the spatial composition of the TME. Various spatial metrics and machine learning approaches were proposed for prediction of either patient survival or gene mutations from this data. Still, these approaches are limited in the scope of analyzed features and in their explainability, and as such fail to transfer to clinical practice.

METHODS

Here, we generated 23,199 image patches from 26 hematoxylin-and-eosin (H&E)-stained lung cancer tissue sections and annotated them into 9 different tissue classes. Using this dataset, we trained a deep neural network ARA-CNN. Next, we applied the trained network to segment 467 lung cancer H&E images from The Cancer Genome Atlas (TCGA) database. We used the segmented images to compute human-interpretable features reflecting the heterogeneous composition of the TME, and successfully utilized them to predict patient survival and cancer gene mutations.

RESULTS

We achieved per-class AUC ranging from 0.72 to 0.99 for classifying tissue types in lung cancer with ARA-CNN. Machine learning models trained on the proposed human-interpretable features achieved a c-index of 0.723 in the task of survival prediction and AUC up to 73.5% for PDGFRB in the task of mutation classification.

CONCLUSIONS

We presented a framework that accurately predicted survival and gene mutations in lung adenocarcinoma patients based on human-interpretable features extracted from H&E slides. Our approach can provide important insights for designing novel cancer treatments, by linking the spatial structure of the TME in lung adenocarcinoma to gene mutations and patient survival. It can also expand our understanding of the effects that the TME has on tumor evolutionary processes. Our approach can be generalized to different cancer types to inform precision medicine strategies.

Preoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement.

AIM

To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy.

METHODS

A total of 150 HCC patients were randomly divided into a training cohort (n = 107) and a validation cohort (n = 43). Radiomics features were extracted from the entire tumour lesion. The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature. Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram, incorporating clinicopathological characteristics and the radiomics signature. The accuracy of the nomogram was assessed with the concordance index, receiver operating characteristic (ROC) curve and calibration curve. The clinical utility was evaluated by decision curve analysis (DCA). Kaplan–Meier methodology was used to compare the survival between the low- and high-risk subgroups.

RESULTS

In total, seven radiomics features were selected to construct the radiomics signature. According to the results of univariate and multivariate Cox regression analyses, alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR) and radiomics signature were included to build the nomogram. The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774, respectively. ROC curve analysis for predicting 1-, 3-, and 5-year OS confirmed satisfactory accuracy [training cohort, area under the curve (AUC) = 0.850, 0.791 and 0.823, respectively; validation cohort, AUC = 0.905, 0.884 and 0.911, respectively]. The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival. DCA curves suggested that the nomogram had more benefit than traditional staging system models. Kaplan–Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival (all P < 0.0001).

CONCLUSION

The nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.

Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data

INTRODUCTION

While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value.

OBJECTIVES

Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies.

METHODS

Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility.

RESULTS

Overall survival (OS) is predicted in external validation cohort with AUROC_TEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROC_TEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased.

CONCLUSION

This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.

Performance of the Paediatric Trauma Score on survival prediction of injured children at a major trauma centre: A retrospective Colombian cohort, 2011-2019

Background

Despite improvements in children's health due to a reduction in infections, trauma continues to cause many deaths among adolescents. Strategies to mitigate morbidity and mortality from trauma include severity scores to classify and refer patients to the appropriate hospitals to provide better management; however, these strategies have not been assessed in Colombian children. This study aimed to describe the characteristics and outcomes of injured children and evaluate the performance of the Pediatric Trauma Score (PTS) in predicting survival at a major trauma centre in a Colombian city.

Methods

This was a retrospective cohort study of children aged <18 years who were treated for injuries at a hospital in Colombia. The primary outcome was 30-day mortality. A simple logistic regression model was used with PTS as the predictor variable and vital status at discharge as the outcome variable. PTS performance was assessed by discrimination using the area under the receiver-operating characteristic (AUROC) curve and by calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test.

Findings

A total of 1047 children were admitted. The median age was 12 years (interquartile range [IQR]=5-15); 73·7% were male, and 66·1% had blunt trauma. The most frequent cause of injury was traffic accident (31·5%) followed by assaults (29%). Mortality was 5·9%; 61·3% of these deaths occurred in adolescents between 15 and 17 years of age and 71% of deaths in this age group were due to injuries from a firearm. The PTS had a median of 7 (IQR=5-9), an AUROC of 0·93, and good calibration (HL=7·97, p = 0·33).

Interpretation

The highest proportion of trauma and death occurred among adolescents. Interpersonal violence was the most frequent cause of death in this age group. The PTS showed good predictive power for survival, with excellent discrimination and good calibration.

Funding

None.

Clinical applications of machine learning in the survival prediction and classification of sepsis: coagulation and heparin usage matter

BACKGROUND

Sepsis is a life-threatening syndrome eliciting highly heterogeneous host responses. Current prognostic evaluation methods used in clinical practice are characterized by an inadequate effectiveness in predicting sepsis mortality. Rapid identification of patients with high mortality risk is urgently needed. The phenotyping of patients will assistant invaluably in tailoring treatments.

METHODS

Machine learning and deep learning technology are used to characterize the patients' phenotype and determine the sepsis severity. The database used in this study is MIMIC-III and MIMIC-IV ('Medical information Mart for intensive care') which is a large, public, and freely available database. The K-means clustering is used to classify the sepsis phenotype. Convolutional neural network (CNN) was used to predict the 28-day survival rate based on 35 blood test variables of the sepsis patients, whereas a double coefficient quadratic multivariate fitting function (DCQMFF) is utilized to predict the 28-day survival rate with only 11 features of sepsis patients.

RESULTS

The patients were grouped into four clusters with a clear survival nomogram. The first cluster (C_1) was characterized by low white blood cell count, low neutrophil, and the highest lymphocyte proportion. C_2 obtained the lowest Sequential Organ Failure Assessment (SOFA) score and the highest survival rate. C_3 was characterized by significantly prolonged PTT, high SIC, and a higher proportion of patients using heparin than the patients in other clusters. The early mortality rate of patients in C_3 was high but with a better long-term survival rate than that in C_4. C_4 contained septic coagulation patients with the worst prognosis, characterized by slightly prolonged partial thromboplastin time (PTT), significantly prolonged prothrombin time (PT), and high septic coagulation disease score (SIC). The survival rate prediction accuracy of CNN and DCQMFF models reached 92% and 82%, respectively. The models were tested on an external dataset (MIMIC-IV) and achieved good performance. A DCQMFF-based application platform was established for fast prediction of the 28-day survival rate.

CONCLUSION

CNN and DCQMFF accurately predicted the sepsis patients' survival, while K-means successfully identified the phenotype groups. The distinct phenotypes associated with survival, and significant features correlated with mortality were identified. The findings suggest that sepsis patients with abnormal coagulation had poor outcomes, abnormal coagulation increase mortality during sepsis. The anticoagulation effects of appropriate heparin sodium treatment may improve extensive micro thrombosis-caused organ failure.

Overlapping group screening for detection of gene-environment interactions with application to TCGA high-dimensional survival genomic data

BACKGROUND

In the context of biomedical and epidemiological research, gene-environment (G-E) interaction is of great significance to the etiology and progression of many complex diseases. In high-dimensional genetic data, two general models, marginal and joint models, are proposed to identify important interaction factors. Most existing approaches for identifying G-E interactions are limited owing to the lack of robustness to outliers/contamination in response and predictor data. In particular, right-censored survival outcomes make the associated feature screening even challenging. In this article, we utilize the overlapping group screening (OGS) approach to select important G-E interactions related to clinical survival outcomes by incorporating the gene pathway information under a joint modeling framework.

RESULTS

Simulation studies under various scenarios are carried out to compare the performances of our proposed method with some commonly used methods. In the real data applications, we use our proposed method to identify G-E interactions related to the clinical survival outcomes of patients with head and neck squamous cell carcinoma, and esophageal carcinoma in The Cancer Genome Atlas clinical survival genetic data, and further establish corresponding survival prediction models. Both simulation and real data studies show that our method performs well and outperforms existing methods in the G-E interaction selection, effect estimation, and survival prediction accuracy.

CONCLUSIONS

The OGS approach is useful for selecting important environmental factors, genes and G-E interactions in the ultra-high dimensional feature space. The prediction ability of OGS with the Lasso penalty is better than existing methods. The same idea of the OGS approach can apply to other outcome models, such as the proportional odds survival time model, the logistic regression model for binary outcomes, and the multinomial logistic regression model for multi-class outcomes.

Risk assessment tools for survival prognosis: An era of new surrogacy endpoints for clinical outcome measurement in pulmonary arterial hypertension clinical trials?

Developing a new medication in a rare disease indication like pulmonary arterial hypertension (PAH) is very challenging. This is especially true now that clinical trials often employ time to clinical worsening (TTCW) as an endpoint (thus requiring a relatively large and lengthy trial) and since patients are more frequently prescribed combination therapy. During the last few decades, several tools have been developed to predict mortality in PAH and have demonstrated generally good discrimination. The objective of this review article is to assess the available data on the different tools and methods described in the literature and identify potential candidates that could be used as surrogate endpoints in pivotal randomized clinical trials in future. Some of these tools have been validated in various registries and in post-hoc analyses of clinical trial data, but none have been assessed in a prospective clinical trial and we still lack the evidence necessary for endorsement by health authorities. In this review, we identify several promising options that warrant further investigation as potential surrogate endpoints in clinical trials to replace TTCW or 6-minute walk distance. Prospective inclusion of such tools in new clinical trials may help build a stronger surrogacy for prognosis of disease progression and mortality.

Prevalence and predictors for 72-h mortality after transfer to acute palliative care unit

Purpose

Accurate prediction of survival is important to facilitate clinical decision-making and improve quality of care at the end of life. While it is well documented that survival prediction poses a challenge for treating physicians, the need for clinically valuable predictive factors has not been met. This study aims to quantify the prevalence of patient transfer 72 h before death onto the acute palliative care unit in a tertiary care center in Switzerland, and to identify factors predictive of 72-h mortality.

Methods

All patients hospitalized between January and December 2020 on the acute palliative care unit of the Competence Center Palliative Care of the Department of Radiation Oncology at the University Hospital Zurich were assessed. Variables were retrieved from the electronic medical records. Univariable and multivariable logistic regressions were used to identify predictors of mortality.

Results

A total of 398 patients were screened, of which 188 were assessed. Every fifth patient spent less than 72 h on the acute palliative care unit before death. In multivariable logistic regression analysis, predictors for 72-h mortality after transfer were no prior palliative care consult (p = 0.011), no advance care directive (p = 0.044), lower performance status (p = 0.035), lower self-care index (p = 0.003), and lower blood albumin level (p = 0.026).

Conclusion

Late transfer to the acute palliative care unit is not uncommon, which can cause additional distress to patients and caretakers. Though clinically practical short-term survival predictors remain largely unidentified, early integration of palliative care should be practiced more regularly in patients with life-limiting illness.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00520-022-07075-6.

Predicting 2-year survival in stage I-III non-small cell lung cancer: the development and validation of a scoring system from an Australian cohort

Background

There are limited data on survival prediction models in contemporary inoperable non-small cell lung cancer (NSCLC) patients. The objective of this study was to develop and validate a survival prediction model in a cohort of inoperable stage I-III NSCLC patients treated with radiotherapy.

Methods

Data from inoperable stage I-III NSCLC patients diagnosed from 1/1/2016 to 31/12/2017 were collected from three radiation oncology clinics. Patient, tumour and treatment-related variables were selected for model inclusion using univariate and multivariate analysis. Cox proportional hazards regression was used to develop a 2-year overall survival prediction model, the South West Sydney Model (SWSM) in one clinic (n = 117) and validated in the other clinics (n = 144). Model performance, assessed internally and on one independent dataset, was expressed as Harrell’s concordance index (c-index).

Results

The SWSM contained five variables: Eastern Cooperative Oncology Group performance status, diffusing capacity of the lung for carbon monoxide, histological diagnosis, tumour lobe and equivalent dose in 2 Gy fractions. The SWSM yielded a c-index of 0.70 on internal validation and 0.72 on external validation. Survival probability could be stratified into three groups using a risk score derived from the model.

Conclusions

A 2-year survival model with good discrimination was developed. The model included tumour lobe as a novel variable and has the potential to guide treatment decisions. Further validation is needed in a larger patient cohort.

An immune-related nomogram model that predicts the overall survival of patients with lung adenocarcinoma

Background

Lung adenocarcinoma accounts for approximately 40% of all primary lung cancers; however, the mortality rates remain high. Successfully predicting progression and overall (OS) time will provide clinicians with more options to manage this disease.

Methods

We analyzed RNA sequencing data from 510 cases of lung adenocarcinoma from The Cancer Genome Atlas database using CIBERSORT, ImmuCellAI, and ESTIMATE algorithms. Through these data we constructed 6 immune subtypes and then compared the difference of OS, immune infiltration level and gene expression between these immune subtypes. Also, all the subtypes and immune cells infiltration level were used to evaluate the relationship with prognosis and we introduced lasso-cox method to constructe an immune-related prognosis model. Finally we validated this model in another independent cohort.

Results

The C3 immune subtype of lung adenocarcinoma exhibited longer survival, whereas the C1 subtype was associated with a higher mutation rate of MUC17 and FLG genes compared with other subtypes. A multifactorial correlation analysis revealed that immune cell infiltration was closely associated with overall survival. Using data from 510 cases, we constructed a nomogram prediction model composed of clinicopathologic factors and immune signatures. This model produced a C-index of 0.73 and achieved a C-index of 0.844 using a validation set.

Conclusions

Through this study we constructed an immune related prognosis model to instruct lung adenocarcinoma’s OS and validated its value in another independent cohost. These results will be useful in guiding treatment for lung adenocarcinoma based on tumor immune profiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01902-6.

Feature screening for survival trait with application to TCGA high-dimensional genomic data

Background

In high-dimensional survival genomic data, identifying cancer-related genes is a challenging and important subject in the field of bioinformatics. In recent years, many feature screening approaches for survival outcomes with high-dimensional survival genomic data have been developed; however, few studies have systematically compared these methods. The primary purpose of this article is to conduct a series of simulation studies for systematic comparison; the second purpose of this article is to use these feature screening methods to further establish a more accurate prediction model for patient survival based on the survival genomic datasets of The Cancer Genome Atlas (TCGA).

Results

Simulation studies prove that network-adjusted feature screening measurement performs well and outperforms existing popular univariate independent feature screening methods. In the application of real data, we show that the proposed network-adjusted feature screening approach leads to more accurate survival prediction than alternative methods that do not account for gene-gene dependency information. We also use TCGA clinical survival genetic data to identify biomarkers associated with clinical survival outcomes in patients with various cancers including esophageal, pancreatic, head and neck squamous cell, lung, and breast invasive carcinomas.

Conclusions

These applications reveal advantages of the new proposed network-adjusted feature selection method over alternative methods that do not consider gene-gene dependency information. We also identify cancer-related genes that are almost detected in the literature. As a result, the network-based screening method is reliable and credible.

Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis

Background

We collected information on patients with rectal adenocarcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a model that combined deep learning with a multilayer neural network (the DeepSurv model) for predicting the survival rate of patients with rectal adenocarcinoma.

Methods

We collected patients with rectal adenocarcinoma in the United States and older than 20 yearswho had been added to the SEER database from 2004 to 2015. We divided these patients into training and test cohortsat a ratio of 7:3. The training cohort was used to develop a seven-layer neural network based on the analysis method established by Katzman and colleagues to construct a DeepSurv prediction model. We then used the C-index and calibration plots to evaluate the prediction performance of the DeepSurv model.

Results

The 49,275 patients with rectal adenocarcinoma included in the study were randomly divided into the training cohort (70%, n = 34,492) and the test cohort (30%, n = 14,783). There were no statistically significant differences in clinical characteristics between the two cohorts (p > 0.05). We applied Cox proportional-hazards regression to the data in the training cohort, which showed that age, sex, marital status, tumor grade, surgery status, and chemotherapy status were significant factors influencing survival (p < 0.05). Using the training cohort to construct the DeepSurv model resulted in a C-index of the model of 0.824, while using the test cohort to verify the DeepSurv model yielded a C-index of 0.821. Thesevalues show that the prediction effect of the DeepSurv model for the test-cohort patients was highly consistent with the prediction resultsfor the training-cohort patients.

Conclusion

The DeepSurv prediction model of the seven-layer neural network that we have established can accurately predict the survival rateand time of rectal adenocarcinoma patients.