Machine Learning Algorithm Guiding Local Treatment Decisions to Reduce Pain for Lung Cancer Patients with Bone Metastases, a Prospective Cohort Study

Abstract Background: As life expectancy increases for lung cancer patients who develop bone metastases, the need for personalized local treatment for bone metastases is expanding.Methods: Lung cancer patients with bone metastases were treated by a multidisciplinary team via surgery, percutaneous osteoplasty, or radiation. The pre- and post-treatment visual analog scale (VAS) and Quality of Life (QoL) scores were analyzed. QoL at 12 weeks was the main outcome. Treatment-related costs and overall survival time (OS) were collected. We used machine learning to develop and test models to predict which patients should receive local treatment. Models discrimination were evaluated by the area under curve (AUC), and the best one was used for validation in clinical use. Results: Under the direction of a multidisciplinary team, 161 patients in the training set, and 32 patients in the test set underwent local treatment. A decision tree model included VAS scale, bone metastases character, Frankel classification, Mirels score, age, driver gene, aldehyde dehydrogenase 2, and enolase 1 expression had a best AUC of 0.92 (95%CI 0.89 to 0.94), and 36 patients in a validation set underwent local treatment guided by the model. Improved QoL and VAS scores were observed at 12 weeks after local treatment in training, test, and validation sets (p < 0.05), with no significant differences among the three datasets. There were no significant differences in mean costs among the three datasets in the four treatment groups. OS was 18.03±0.45 months and did not significantly differ among treatment groups or the three datasets. Conclusions: Local treatment not only had no negative influence on OS but also provided significant pain relief and improved QoL. QoL, OS or costs did not significantly differ between patients whose treatment was guided by a multidisciplinary team or machine learning model. Our machine learning model using clinical data can help guide clinicians to make local treatment decisions to improve patients’ QoL.Trial registration: No. ChiCRT-ROC-16009501

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Machine learning to identify lung cancer with tuberculosis from isolated tuberculosis (Preprint)

10.2196/preprints.35101 ◽

2021 ◽

Author(s):

Zhenhao Li

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Cancer Patients ◽

Tumor Markers ◽

Learning Algorithm ◽

Laboratory Data ◽

Pathological Examination ◽

Training Set ◽

Lung Cancer Patients ◽

Tb Pcr

UNSTRUCTURED Tuberculosis (TB) is a precipitating cause of lung cancer. Lung cancer patients coexisting with TB is difficult to differentiate from isolated TB patients. The aim of this study is to develop a prediction model in identifying those two diseases between the comorbidities and TB. In this work, based on the laboratory data from 389 patients, 81 features, including main laboratory examination of blood test, biochemical test, coagulation assay, tumor markers and baseline information, were initially used as integrated markers and then reduced to form a discrimination system consisting of 31 top-ranked indices. Patients diagnosed with TB PCR ＞1mtb/ml as negative samples, lung cancer patients with TB were confirmed by pathological examination and TB PCR ＞1mtb/ml as positive samples. We used Spatially Uniform ReliefF (SURF) algorithm to determine feature importance, and the predictive model was built using machine learning algorithm Random Forest. For cross-validation, the samples were randomly split into four training set and one test set. The selected features are composed of four tumor markers (Scc, Cyfra21-1, CEA, ProGRP and NSE), fifteen blood biochemical indices (GLU, IBIL, K, CL, Ur, NA, TBA, CHOL, SA, TG, A/G, AST, CA, CREA and CRP), six routine blood indices (EO#, EO%, MCV, RDW-S, LY# and MPV) and four coagulation indices (APTT ratio, APTT, PTA, TT ratio). This model presented a robust and stable classification performance, which can easily differentiate the comorbidity group from the isolated TB group with AUC, ACC, sensitivity and specificity of 0.8817, 0.8654, 0.8594 and 0.8656 for the training set, respectively. Overall, this work may provide a novel strategy for identifying the TB patients with lung cancer from routine admission lab examination with advantages of being timely and economical. It also indicated that our model with enough indices may further increase the effectiveness and efficiency of diagnosis.

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