A novel clinical tool to predict cancer‐specific survival in patients with primary pelvic sarcomas: A large population‐based retrospective cohort study

Huang, Chao; Su, Qiang; Ding, Zichuan; Zeng, Wenbin; Zhou, Kai

doi:10.1002/cam4.4998

Cited by 5 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While in classification prediction they achieved Area under the receiver operating characteristic curve (AUC) of 0.91 ± 0.03 and area under the precision-recall curve (AUPR) of 0.62 ± 0.05. Huang et al 70 proposed Kaplan–Meier method and univariate Cox regression analysis for predictive accuracy and clinical utility of the nomogram through a calibration curve. They used ROC curve, and decision curve analysis (DCA).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Abd El-Hafeez,

Shams,

Elshaier

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O’Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Abd El-Hafeez,

Shams,

Elshaier

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Cancer prevention [16][17][18] , cancer detection ,19-21 , COVID-19 detection [22][23][24] , medical information analysis [25][26][27] , and many other medical fields have used the efficiency of ML and DL models.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction

Jassim,

Abed,

Saied

2024

Baghdad Sci.J

View full text Add to dashboard Cite

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a CT lung cancer dataset consisting of 1000 images and four different classes. The data augmentation process is applied to prevent overfitting, increase the size of the data, and enhance the training process. Score-level fusion and ensemble learning are also used to get the best performance and solve the low accuracy problem. All models were evaluated using accuracy, precision, recall, and the F1-score. Results: Experiments show the high performance of the ensemble model with 99.44% accuracy, which is better than all of the current state-of-the art methodologies. Conclusion: The current study's findings demonstrate the high accuracy and robustness of the proposed ensemble transfer deep learning using various transfer learning models

show abstract

A novel clinical tool to predict cancer‐specific survival in patients with primary pelvic sarcomas: A large population‐based retrospective cohort study

Huang

Ding

et al. 2022

Cancer Medicine

View full text Add to dashboard Cite

Background: Primary osseous sarcoma of the pelvis is rare and has a particularly sinister outcome. This study aims to identify independent prognostic factors of cancer-specific survival (CSS) in patients with primary pelvic sarcoma (PS) and develop a nomogram to predict 3-, 5-, and 10-year probability of CSS in these patients. Methods:The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 416 patients with primary PS, who were divided into two groups: a training cohort and a validation cohort. Univariate and multivariate Cox analyses were used to screen independent prognostic factors in patients with primary PS. Based on these independent prognostic factors, a prognostic nomogram was developed to predict 3-, 5-, and 10-year probability of CSS. The nomogram's predictive performance and clinical value were evaluated using the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Finally, a mortality risk stratification system was developed.Results: Tumor size, tumor stage, histological type, surgery, and chemotherapy were identified as independent prognostic factors for the CSS of primary PS patients. Based on these factors, a nomogram was created to predict the 3-, 5-, and 10-year probability of CSS in these patients. The calibration curve, ROC, and DCA indicated that the nomogram performed well and was appropriate for clinical use, with 3-, 5-, and 10-year areas under ROC curve all higher than 0.800. Furthermore, the nomogram-based mortality risk stratification system could effectively divide these patients into three risk subgroups. Conclusions:The nomogram constructed in this study could accurately predict 3-, 5-, and 10-year probability of CSS in patients with primary PS. Clinicians can use the nomogram to categorize these patients into risk subgroups and provide personalized treatment plans.

show abstract

A novel clinical tool to predict cancer‐specific survival in patients with primary pelvic sarcomas: A large population‐based retrospective cohort study

Cited by 5 publications

References 33 publications

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs

Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction

A novel clinical tool to predict cancer‐specific survival in patients with primary pelvic sarcomas: A large population‐based retrospective cohort study

Contact Info

Product

Resources

About