lung cancer machine learning

New lung-cancer drugs extend survival times, Lung-cancer researchers and clinicians must pay more attention to women, Better treatments on the way for lung cancer that spreads to the brain, Oncogene-specific advocacy groups bring a patient-centric perspective to studies of lung cancer, How liquid biopsies allow smarter lung-cancer treatment, Patterns of tumour transcriptional variability, In situ tumour arrays reveal early environmental control of cancer immunity, Pan-KRAS inhibitor disables oncogenic signalling and tumour growth, The sleight-of-hand trick that can simplify scientific computing. In the detection performance test, metrics were evaluated on a per-lesion basis. Image Anal. Our research refers to the majority class with (Short LOS), and the minority is the (Long LOS). Knaus, W. A., Zimmerman, J. E., Wagner, D. P., Draper, E. A. Artificial intelligence is improving the detection of lung cancer - Nature Most hospital managements use electronic healthcare records (EHR) to facilitate their daily operational and medical procedures and LOS determination. Google Scholar. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Machine-learning algorithms for asthma, COPD, and lung cancer risk We found several studies that used classification or detection methods to detect lung cancer on chest radiographs, but not the segmentation method. CAS Lung Cancer Detection System Using Image Processing and Machine J. Occup. 38(1), 223230 (2011). We have implemented three machine learning predictive models to assess the proposed Lung Cancer LOS (see Additional file: S6.1-S6.3). Further, to assess their feasibility and the clinical insights they may induce for utilizing hospital resources and hospital healthcare as- assessment systems in the ICU. & Rashidi, P. Deep ehr: a survey of recent advances in deep learning techniques for electronic health record (ehr) analysis. volume12, Articlenumber:607 (2022) There were no attempts to utilize the advancement of explainable artificial intelligence (xAI) methods that aimed to explain the decision-making and working inners of machine learning models for better understanding of the data-driven insights and, therefore, improving the hospitals booking of facilities and resources utilization. Radiology 290, 218228. Development and validation of deep learningbased automatic detection algorithm for malignant pulmonary nodules on chest radiographs. All other outputs were FPs. A limited number of cancer-based studies assessed the predictive models in the context of lung cancer LOS from EHR and data-driven using machine learning algorithms. IVBH Presents Groundbreaking Data for Early Lung Cancer - BioSpace Finally, the model tuning stage is applied to the outperforming model. Cite this article. Radiology 294, 199209. B.A., O.A., M.A., O.M., F.A., and O.D. With regard to metastatic nodules, 33% (14/43) metastatic lung cancers were FNs. Scientific Reports (Sci Rep) While RF SHAP (SMOTE ENN ) ranked (systolic) variable in the top features, the diastolic came in the least in features by importance in the list. Our research motivation takes the opportunity to explore the LOS prediction as a health assessment metric for resource utilization in the ICU settings with ML classification approaches. It was difficult for the model to identify lung cancers that overlapped with blind spots even when the tumor size was large (Fig. The image on the left is an original image, and the image on the right is an image output by our model. By submitting a comment you agree to abide by our Terms and Community Guidelines. . The model detected the nodule in the right middle lung field. Finally, we did not study the relative accuracy of the model compared to clinician estimates of LOS. This study aims to develop a predictive machine learning research framework to predict lung cancer inpatients length of stay at the time of ICU admission based on the data fed to the ML models from the electronic hospital medical records. The Chinese University of Hong Kong, Shenzhen (CUHK Shenzhen). Eco. Then, the outperforming model is further evaluated based on the study motivation. PubMed Diagnosis (Berl) 1, 7984. reviewed critical revisions to the manuscript. Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis - ScienceDirect Genomics, Proteomics & Bioinformatics Volume 20, Issue 5, October 2022, Pages 850-866 REVIEW Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis Yawei Li 1 , Xin Wu 2 , Ping Yang 3 , Guoqian Jiang 4 , Yuan Luo 1 Add to Mendeley Confusion matrix for class-balancing techniques for lung cancer LOS with CS. Prediction and Classification of Lung Cancer Using Machine Learning performed all analyses, using R version 3.6.0 (https://www.r-project.org/). Model development was performed by A.C. and Y.S. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 28, 746750. Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning Haike Lei 1, Mengyang Zhang 2, Zeyi Wu 2, Chun Liu 2, Xiaosheng Li 1, Wei Zhou 1, Bo Long 1, Jiayang Ma 2, Huiyi Zhang 2, Ying Wang 1, Guixue Wang 3, Mengchun Gong 2, Na Hong 2, Haixia Liu 1* and Yongzhong Wu 1* IEEE Access 7, 110710110721 (2019). Identification of long non-coding RNA (lncRNA) signatures could be used to improve cancer clinical outcome. When we investigated FNs, we found that nodules in blind spots and metastatic nodules tended to be FNs. A 68-year-old man with a mass in the left lower lobe that was diagnosed as adenocarcinoma. Moreover, these techniques are broadly generalizable, and scientists can build ensembles based on these algorithms to predict many other clinical outcomes. https://doi.org/10.1001/jamanetworkopen.2020.17135 (2020). (2020). In Intensive Care Medicine, vol. Since the segmentation method has more information about the detected lesions than the classification or detection methods, it has advantages not only in the detection of lung cancer but also in follow-up and treatment efficacy. and JavaScript. The purpose of this study was to train and validate a DL-based model capable of detecting lung cancer on chest radiographs using the segmentation method, and to evaluate the characteristics of this DL-based model to improve sensitivity while maintaining low FP results. 68, 394424. Compared with CT, chest radiographs have advantages in terms of accessibility, cost effectiveness, and low radiation dose. Yoo, H., Kim, K. H., Singh, R., Digumarthy, S. R. & Kalra, M. K. Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs. Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Am. Nature Med. In this study, we included only chest radiographs containing malignant nodules/masses. https://doi.org/10.1117/12.955926 (1977). Subsequently, under-sampling methods are not suitable for predicting inpatients Length of Stay. This means that lesions overlapping blind spots were not only difficult to detect, but also had low accuracy in segmentation. Example of one false positive case. Typically, a series of pre-processing steps using statistical methods and pretrained CNNs for feature extraction are carried out from several input sources (mostly images) to delineate the . Predictors of short-term mortality in critically ill patients with solid malignancies. volume12, Articlenumber:727 (2022) Sensitivity was lower in lung cancers that overlapped with blind spots such as pulmonary apices, pulmonary hila, chest wall, heart, and sub-diaphragmatic space (0.500.64) compared with those in non-overlapped locations (0.87). All authors contributed to the study conception and design. Med. A nomogram for predicting long length of stay in the intensive care unit in patients undergoing cabg: Results from the multicenter e-cabg registry. Keegan, M. T., Gajic, O. Daiju Ueda has no relevant relationships to disclose. Figure 1 shows a flowchart of the eligibility criteria for the chest radiographs. Sci Rep 12, 607 (2022). J. ISSN 2045-2322 (online). Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method, https://doi.org/10.1038/s41598-021-04667-w. Get what matters in cancer research, free to your inbox weekly. First, images are acquired. The MIMIC-III is a relational database consisting of data tables relating to patients who stayed at the ICU BIDMC hospital. N. Engl. PubMed Sun, L. Y., Bader Eddeen, A., Ruel, M., MacPhee, E. & Mesana, T. G. Derivation and validation of a clinical model to predict intensive care unit length of stay after cardiac surgery. In these 20 FPs, 13 overlapped with blind spots. Admission of advanced lung cancer patients to intensive care unit: a retrospective study of 76 patients. Google Scholar. ADASYN distinguished distinctively two classes (Short LOS and Long LOS), where the RF did not report any false positive or false negative predictions. In this article, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Surveillance is universally recommended for non-small cell lung cancer (NSCLC) patients treated with curative-intent radiotherapy. For example, beds managers could ensure that adequate numbers of beds are available in intensive care units. In clinical practice, classifying the size of a lesion at the pixel-level increases the likelihood of making a correct diagnosis. Pecoraro, F., Clemente, F. & Luzi, D. The efficiency in the ordinary hospital bed management in italy: an in-depth analysis of intensive care unit in the areas affected by covid-19 before the outbreak. 37, 1533. Hence, we are referring to CS approach when discussing the reported results with class-balancing (AUC) performance measures. Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis The LR is currently in used LOS, such as14,23 predictive problems. Sci. Takashi Honjo has no relevant relationships to disclose. We retrospectively collected consecutive chest radiographs from patients who had been pathologically diagnosed with lung cancer at our hospital. Open 3, e2017135. Moreover, the specificity dropped down drastically, and both class-balancing methods reached the lowest specificity scores (11% and 4%) correspondingly in both approaches (CS and RFE) (Fig. This is often not a problem in clinical practice. In other word, there is a possibility that the model could misidentify the lesion as a malignant if the features of calcification that should signal a benign lesion are masked by normal anatomical structures. Deep learning classification of lung cancer histology using CT images https://doi.org/10.1515/dx-2013-0012 (2014). Blom, M. C. et al. 3). For example, Dong et al.26 analyzed the effectiveness of oxygen desaturation (EOD) and heart rate to predict major postoperative cardiopulmonary complications for non-small cell lung cancer patients using binary logistic regression. Cancer 45, 2633. Thus, the FROC curve shows sensitivity as a function of the number of FPs shown on the image. 1 on the test dataset. Our study reports several important findings. Res. PLoS ONE 15, e0239249 (2020). Under-sampling methods reported the least important AUC results (50%, with CI 95%: 40.259.8%) for both (ENN and Tomek- Links). Lung cancer screening: The Mayo program. Lung cancer LOS predictive framework in ICU settings. Machine Learning for Early Lung Cancer Identification Using Routine Data6, 118 (2019). }, author={Chunli Kong and Linqiang Lai and Xiaofeng Jin and Weiyue Chen and Jiayi Ding and Liyun Zheng and Dengke Zhang and Xihui Ying and Xiaoxiao Chen and . The DL-based model was able to detect lung cancers on chest radiographs, with low mFPI. The inclusion criteria were as follows: (a) pathologically proven lung cancer in a surgical specimen; (b) age>40years at the time of the preoperative chest radiograph; (c) chest CT performed within 1month of the preoperative chest radiograph. Health 36, 345359 (2015). https://doi.org/10.1097/00043764-198608000-00038 (1986). PubMed The EHR healthcare assessment systems store data associated with patients encounters, such as their demographics, diagnosis, laboratory tests, prescriptions, radiological images, clinical notes, and many more12,13. Accurate Modelling of this outcome can help healthcare systems identify risk factors for unnecessary hospital days of stay, potentially reduce waste, provide more efficient allocation of medical resources, and improve patient health care. Unlike other models, RF improved by adding more features in each (RFE) feature selection performance attesting with cross-validation (k-fold = 10). Internet Explorer). CAS Furthermore, the class balancing with Over-sampling such as ADASYN and SMOTE achieved the most outstanding AUC and G.Mean results, followed by the over/and under-sampling methods. ADS This search indicated that, despite an abundance of studies using machine learning with radiology to detect and diagnose lung cancer, there is a scarcity of studies related to its use as a tool for prognostication. You are using a browser version with limited support for CSS. Data 3, 19 (2016). With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3100%, and 100% respectively). Our study represents the potential of machine learning to predict the Length of Stay of ICU cancer-based hospitalization in particular lung cancer patients efficiently. Informed consent was not required because all protected health information has been de-identified. Nevertheless, logistic regression was the fastest model to train, whereas the RF and XGBoost both models needed more time to solve their prediction outcomes. Access Nature and 54 other Nature Portfolio journals, Get Nature+, our best-value online-access subscription, Receive 51 print issues and online access, Get just this article for as long as you need it, Prices may be subject to local taxes which are calculated during checkout, doi: https://doi.org/10.1038/d41586-020-03157-9. Here, in . The sensitivity of lesions with traceable edges on radiographs was 0.87, and that for untraceable edges was 0.21. The use of residual connections not only avoids the degradation problem caused by deep structures but also reduces the training time. tumor compressing the main bronchus), presence of recurrent laryngeal nerve paralysis causing hoarseness of voice and aspiration in the lungs may lead to increasing the LOS of patients admitted to the ICU. In the meantime, to ensure continued support, we are displaying the site without styles We did not observe a data-driven machine learning approach treating the imbalance class common problem in the predictive classifier. BMC Cancer 11, 19 (2011). If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. On the other hand, the segmentation accuracy was relatively high for lesions that were detected by the modeleven if they overlapped withthe blind spots. Rocheteau, E., Li, P. & Hyland, S. Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit. 30, 13591368. The methodology section presents accurate classification and prediction of lung cancer using machine learning and image processing-enabled technology. 62, 132137. The lesion was identifiable by the model because its edges were traceable. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Akitoshi Shimazaki has no relevant relationships to disclose. J. Cardiothorac. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in In practical application, the proposed methodological framework can be pipelined to achieve optimal predictive performance by allowing clinicians and beds managers to affirm the outperforming post-features selection method against the model choice. The following points summarize the contributions of this article: This study developed a deep learning-based model for detection and segmentation of lung cancer on chest radiographs. Article The literature survey section contains a review of various techniques for the classification and detection of cancer using image processing and classification. On the basis of primary tumor, regional lymph nodes, metastasis, age, and histology type, a prognostic system for lung cancer was created to improve the patient stratification and survival prediction of the TNM. Unlike the Over-sampling or the combination approach, the Under-sampling presented the weakest AUC results (50%) for both TomekLinks and ENN. The dice coefficient for all 71lesions overlapping blind spots was 0.340.38 (SD). Similarly, SMOTE (Over-sampling ) efficaciously predicted (Short/Long) LOS classes with (TP: 44.44% and TN: 53.33%). Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. In addition, both methods reported noticeable false negatives for how RF incorrectly predicts the positive class following both approaches. Ahmad, M.A., Eckert, C. & Teredesai, A. Interpretable machine learning in healthcare. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. We fitted the six class balancing methods into the RF classifier and evaluated their performance (AUC). and T.H. S2 online shows visualized images of the first and last layers. Other scenarios include superior vena cava syndrome when the cancer compresses the superior vena cava causing decreased oxygenation and fluid retention in the upper part of the patients chest, or when there is massive pericardial effusion or heart failure; all of these scenarios necessitate longer ICU stay. In fact, the radiologists had overlooked most of the small metastatic nodules at first and could only identify them retrospectively, with knowledge of the type of lung cancer and their locations. Bray, F. et al. Article Stephan Sloth Lorenzen, Mads Nielsen, Martin Sillesen, Shinya Iwase, Taka-aki Nakada, Eiryo Kawakami, Min Hyuk Choi, Dokyun Kim, Seok Hoon Jeong, Aida Brankovic, David Rolls, Sankalp Khanna, E. Schwager, K. Jansson, J. J. Frassica, Selin Gumustop, Sebastian Gallo-Bernal, Oleg S. Pianykh, Yohann M. Chiu, Josiane Courteau, Catherine Hudon, Scientific Reports The nodule overlapped with the heart (arrows). The feature selection procedure is substantial in feature engineering to identify and select a subset of input variables (attributes) most relevant to the target class. Mu Sook Lee, Yong Soo Kim, Byoung-Dai Lee, Sun Yeop Lee, Sangwoo Ha, Jong Mun Choi, Manuel Schultheiss, Sebastian A. Schober, Daniela Pfeiffer, Viacheslav V. Danilov, Diana Litmanovich, Yuriy Gankin, Manuel Schultheiss, Philipp Schmette, Daniela Pfeiffer, Yu-Xing Tang, You-Bao Tang, Ronald M. Summers, Liding Yao, Xiaojun Guan, Minming Zhang, Scientific Reports Technically, all areas other than the malignant nodules/masses could be trained as normal areas. Res. & Afessa, B. Int. All authors read and approved the final manuscript. 3. Int. Using ML explainable technique called SHAP, we explained the outcome of the predictive model (RF) with SMOTE class balancing technique to understand the most significant clinical features that contributed to predicting lung cancer LOS with the RF model. The machine learning approach, combined with blood-based RNA gene expression profiles, and available demographics and clinical data, is immediately scalable and holds tremendous potential for guiding clinically actionable decisions across the entire lung cancer care continuum, and a promising new direction for early detection for a wide variety . 26, 10541062 (2020). Biomed. Chest 142, 851858 (2012). 2. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Machine learning systems for early detection could save lives. In terms of the SHAP (RF) model explainability for the class balancing techniques (Supplementary file: Fig. Article The training dataset was comprised of chest radiographs obtained between January 2006 and June 2017, and the test dataset contained those obtained between July 2017 and June 2018. https://doi.org/10.1148/radiol.2018180237 (2019). Provided by the Springer Nature SharedIt content-sharing initiative, Signal, Image and Video Processing (2023), Journal of Cancer Research and Clinical Oncology (2023).
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