Development of risk prediction models for lung cancer based on tumor markers and radiological signs

Zha

Clinical Laboratory Analysis

et al. 2021

Background Periostin (POSTN) is an extracellular matrix protein that is overexpressed in lung cancer and is considered an effective diagnostic and prognostic biomarker for lung cancer. The purpose of this study was to investigate the diagnostic performance of POSTN and to further evaluate the diagnostic value of POSTN combined with carcinoembryonic antigen (CEA) and cancer ratio [CR: serum lactate dehydrogenase (LDH)/pleural effusion adenosine deaminase (PE ADA)] in lung cancer‐related malignant PE (MPE). Methods A total of 108 patients with PE, including 54 with lung cancer and 54 with benign lung disease, were enrolled in this study. The POSTN levels of PE and serum were detected using an enzyme‐linked immunosorbent assay. Information on the expression of PE and serum CEA, serum LDH, and PE ADA was collected from medical records. Results The levels of PE POSTN in MPE of patients with lung cancer were significantly higher than those in patients with benign PE (p < 0.0001). The receiver operating characteristic (ROC) curve indicated that the diagnostic sensitivity and specificity of PE POSTN for lung cancer‐related MPE were respectively 77.78% and 68.52% when the cutoff value was determined to be 53.45 ng/ml. The ROC curve analysis demonstrated that PE POSTN has a high diagnostic value in MPE associated with lung cancer [area under the curve (AUC) = 0.764], and the combination of PE POSTN, PE CEA, and CR can improve the diagnostic accuracy of lung cancer‐related MPE (AUC = 0.948). Conclusion POSTN can be used as a potential marker for lung cancer‐related MPE diagnosis.

Section: Discussionsupporting

confidence: 50%

Diagnostic value of periostin in lung cancer‐related malignant pleural effusion

Zha

Clinical Laboratory Analysis

et al. 2021

“…With the development of artificial intelligence technology, the machine learning models provided a better alternative for creating applicable predictive clinical diagnosis tools. In this study, we developed and validated a diagnostic nomogram model to improve the diagnostic accuracy of lung cancer based on AI tools and clinical data ( 3 , 10 , 13 ).…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a nomogram model for lung cancer based on radiomics artificial intelligence score and clinical blood test data

Saber

et al. 2023

Front. Oncol.

BackgroundArtificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer.MethodsBetween 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis.FindingsA total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group.ConclusionThe nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.

“…Guo et al [ 33 ] proposed a deep neural network framework to detect lung nodules from low-dose CT images and determine them benign or malignant to identify lung cancer with an accuracy of 99.02%. Accurately predicting the risk of malignant pulmonary lesions in pleural effusion allows for the early diagnosis of lung cancer [ 34 ]. Ahmad et al [ 35 ] used a random forest, decision tree algorithm for the early diagnosis prediction of lung cancer by analyzing the data of multiple risk factors and pulmonary symptoms.…”

Section: Related Workmentioning

confidence: 99%

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

Zhan

Long

Gou

et al. 2021

Sensors

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.