Development of a machine learning-based multimode diagnosis system for lung cancer

Duan, Shuyin; Cao, Huimin; Hong, Lu; Miao, Lijun; Wang, Jing; Zhou, Xiaolei; Wang, Wei; Hu, Pingzhao; Qu, Lingbo; Wu, Yongjun

doi:10.18632/aging.103249

Cited by 9 publications

(5 citation statements)

References 37 publications

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“…Biomarkers and imaging are often used in combination to improve diagnostic accuracy [ 8 , 28 ], our results also showed that the efficiency has been greatly improved when CAC is combined with PNAIDS in the diagnosis of lung nodules. Correlation analysis further suggested that PNAIDS and CACs are independent of each other, which is consistent with the premise of the model that variables are independent.…”

Section: Discussionsupporting

confidence: 57%

Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules

et al. 2022

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Background The accuracy of CT and tumour markers in screening lung cancer needs to be improved. Computer-aided diagnosis has been reported to effectively improve the diagnostic accuracy of imaging data, and recent studies have shown that circulating genetically abnormal cell (CAC) has the potential to become a novel marker of lung cancer. The purpose of this research is explore new ways of lung cancer screening. Methods From May 2020 to April 2021, patients with pulmonary nodules who had received CAC examination within one week before surgery or biopsy at First Affiliated Hospital of Zhengzhou University were enrolled. CAC counts, CT scan images, serum tumour marker (CEA, CYFRA21–1, NSE) levels and demographic characteristics of the patients were collected for analysis. CT were uploaded to the Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) to assess the malignancy probability of nodules. We compared diagnosis based on PNAIDS, CAC, Mayo Clinic Model, tumour markers alone and their combination. The combination models were built through logistic regression, and was compared through the area under (AUC) the ROC curve. Results A total of 93 of 111 patients were included. The AUC of PNAIDS was 0.696, which increased to 0.847 when combined with CAC. The sensitivity (SE), specificity (SP), and positive (PPV) and negative (NPV) predictive values of the combined model were 61.0%, 94.1%, 94.7% and 58.2%, respectively. In addition, we evaluated the diagnostic value of CAC, which showed an AUC of 0.779, an SE of 76.3%, an SP of 64.7%, a PPV of 78.9%, and an NPV of 61.1%, higher than those of any single serum tumour marker and Mayo Clinic Model. The combination of PNAIDS and CAC exhibited significantly higher AUC values than the PNAIDS (P = 0.009) or the CAC (P = 0.047) indicator alone. However, including additional tumour markers did not significantly alter the performance of CAC and PNAIDS. Conclusions CAC had a higher diagnostic value than traditional tumour markers in early-stage lung cancer and a supportive value for PNAIDS in the diagnosis of cancer based on lung nodules. The results of this study offer a new mode of screening for early-stage lung cancer using lung nodules.

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Section: Discussionsupporting

confidence: 57%

Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules

et al. 2022

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“…In lung cancer, mixed models combining multiple factors have been shown to provide excellent prognostic benefits 12,13 . At present, many studies have tried to establish models to achieve the intelligent identification of benign and malignant nodules, and have shown that machine learning plays an irreplaceable role in disease diagnosis 14,15 …”

Section: Introductionmentioning

confidence: 99%

Using a risk model for probability of cancer in pulmonary nodules

Liu

Song

et al. 2021

Thoracic Cancer

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Background Considering the high morbidity and mortality of lung cancer and the high incidence of pulmonary nodules, clearly distinguishing benign from malignant lung nodules at an early stage is of great significance. However, determining the kind of lung nodule which is more prone to lung cancer remains a problem worldwide. Methods A total of 480 patients with pulmonary nodule data were collected from Shandong, China. We assessed the clinical characteristics and computed tomography (CT) imaging features among pulmonary nodules in patients who had undergone video‐assisted thoracoscopic surgery (VATS) lobectomy from 2013 to 2018. Preliminary selection of features was based on a statistical analysis using SPSS. We used WEKA to assess the machine learning models using its multiple algorithms and selected the best decision tree model using its optimization algorithm. Results The combination of decision tree and logistics regression optimized the decision tree without affecting its AUC. The decision tree structure showed that lobulation was the most important feature, followed by spiculation, vessel convergence sign, nodule type, satellite nodule, nodule size and age of patient. Conclusions Our study shows that decision tree analyses can be applied to screen individuals for early lung cancer with CT. Our decision tree provides a new way to help clinicians establish a logical diagnosis by a stepwise progression method, but still needs to be validated for prospective trials in a larger patient population.

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“…Machine learning (ML), a branch of artificial intelligence and computer science, focusing on simulating the way the human brain learns using statistics and algorithms to improve accuracy, has had an outstanding performance in disease diagnosis, prognosis prediction, antitumor drug response, and treatment response assessment [20][21][22][23][24][25]. However, studies evaluating tumor response in LARC after NAT using ML algorithms are limited.…”

Section: Introductionmentioning

confidence: 99%

Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost

Chen

Wang

Jun-guo

et al. 2022

Int J Colorectal Dis

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Purpose Watch and wait strategy is a safe and effective alternative to surgery in patients with locally advanced rectal cancer (LARC) who have achieved pathological complete response (pCR) after neoadjuvant therapy (NAT); present restaging methods do not meet clinical needs. This study aimed to construct a machine learning (ML) model to predict pCR preoperatively. Methods LARC patients who received NAT were included to generate an extreme gradient boosting-based ML model to predict pCR. The group was divided into a training set and a tuning set at a 7:3 ratio. The SHapley Additive exPlanations value was used to quantify feature importance. The ML model was compared with a nomogram model developed using independent risk factors identified by conventional multivariate logistic regression analysis. Results Compared with the nomogram model, our ML model improved the area under the receiver operating characteristics from 0.72 to 0.95, sensitivity from 43 to 82.2%, and specificity from 87.1 to 91.6% in the training set, the same trend applied to the tuning set. Neoadjuvant radiotherapy, preoperative carbohydrate antigen 125 (CA125), CA199, carcinoembryonic antigen level, and depth of tumor invasion were significant in predicting pCR in both models. Conclusion Our ML model is a potential alternative to the existing assessment tools to conduct triage treatment for patients and provides reference for clinicians in tailoring individual treatment: the watch and wait strategy is used to avoid surgical trauma in pCR patients, and non-pCR patients receive surgical treatment to avoid missing the optimal operation time window.

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Development of a machine learning-based multimode diagnosis system for lung cancer

Cited by 9 publications

References 37 publications

Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules

Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules

Using a risk model for probability of cancer in pulmonary nodules

Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost

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