Purpose: The objective of this study was to create and validate a machine learning (ML)-based model for predicting the likelihood of lung infections following chemotherapy in patients with lung cancer.
Methods: A retrospective study was conducted on a cohort of 502 lung cancer patients undergoing chemotherapy. Data on age, Body Mass Index (BMI), underlying disease, chemotherapy cycle, number of hospitalizations, and various blood test results were collected from medical records. We used the Synthetic Minority Oversampling Technique (SMOTE) to handle unbalanced data. Feature screening was performed using the Boruta algorithm and The Least Absolute Shrinkage and Selection Operator (LASSO). Subsequently, six ML algorithms, namely Logistic Regression (LR), Random Forest (RF), Gaussian Naive Bayes (GNB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were employed to train and develop an ML model using a 10-fold cross-validation methodology. The model's performance was evaluated through various metrics, including the area under the receiver operating characteristic curve (ROC), accuracy, sensitivity, specificity, F1 score, calibration curve, decision curves, clinical impact curve, and confusion matrix. In addition, model interpretation was performed by the Shapley Additive Explanations (SHAP) analysis to clarify the importance of each feature of the model and its decision basis. Finally, we constructed nomograms to make the predictive model results more readable.
Results: The integration of Boruta and LASSO methodologies identified Gender, Smoke, Drink, Chemotherapy cycles, pleural effusion (PE), Neutrophil-lymphocyte count ratio (NLR), Neutrophil-monocyte count ratio (NMR), Lymphocytes (LYM) and Neutrophil (NEUT) as significant predictors. The LR model demonstrated superior performance compared to alternative ML algorithms, achieving an accuracy of 81.80%, a sensitivity of 81.1%, a specificity of 82.5%, an F1 score of 81.6%, and an AUC of 0.888(95%CI(0.863-0.911)). Furthermore, the SHAP method identified Chemotherapy cycles and Smoke as the primary decision factors influencing the ML model's predictions. Finally, this study successfully constructed interactive nomograms and dynamic nomograms.
Conclusion: The ML algorithm, combining demographic and clinical factors, accurately predicted post-chemotherapy lung infections in cancer patients. The LR model performed well, potentially improving early detection and treatment in clinical practice.
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http://dx.doi.org/10.3389/fonc.2024.1403392 | DOI Listing |
Langenbecks Arch Surg
December 2024
Department of Surgery, TUM Universitätsklinikum Klinikum Rechts der Isar Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany.
Objective: Splenectomy is regularly performed in total and distal pancreatectomy due to technical reasons, lymph node dissection and radicality of the operation. However, the spleen serves as an important organ for competent immune function, and its removal is associated with an increased incidence of cancer and a worse outcome in some cancer entities (Haematologica 99:392-398, 2014; Dis Colon Rectum 51:213-217, 2008; Dis Esophagus 21:334-339, 2008). The impact of splenectomy in pancreatic cancer is not fully resolved (J Am Coll Surg 188:516-521, 1999; J Surg Oncol 119:784-793, 2019).
View Article and Find Full Text PDFCancer Cell Int
December 2024
Department of Applied Chemistry, Graduate Institute of Biomedicine and Biomedical Technology, National Chi Nan University, Puli, Taiwan.
Introduction: Chronic alcohol consumption and tobacco usage are major risk factors for esophageal squamous cell carcinoma (ESCC). Excessive tobacco and alcohol consumption lead to oxidative stress and the generation of reactive carbonyl species (RCS) which induce DNA damage and cell apoptosis. This phenomenon contributes to cell damage and carcinogenesis in various organs including ESCC.
View Article and Find Full Text PDFClin Lymphoma Myeloma Leuk
November 2024
Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Background: The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest computed tomography (CT) is reported to have high sensitivity; however, given the limited availability of chest CT during a pandemic, the assessment of more readily available imaging, such as chest radiographs, augmented by artificial intelligence may substitute for the detection of the features of coronavirus disease 2019 (COVID-19) pneumonia.
Methods: We trained a deep convolutional neural network to detect SARS-CoV-2 pneumonia using publicly available chest radiography imaging data including 8,851 normal, 6,045 pneumonia, and 200 COVID-19 pneumonia radiographs.
Ann Thorac Surg
December 2024
Thoracic Surgery Service, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065. Electronic address:
Rev Esp Med Nucl Imagen Mol (Engl Ed)
December 2024
Department of Medical Oncology, Prof. Dr. Cemil Taşcıoğlu City Hospital, Istanbul, Turkey.
Objective: Overexpression of Human Epidermal Growth Factor Receptor 2 (HER2) is thought to be more aggressive in gastric cancer. This study aimed to evaluate the predictability of HER2 status and other prognostic pathologic parameters using [F]FDG PET/CT and to investigate its impact on survival.
Methods: Pretreatment metabolic parameters measured by [F]FDG PET/CT as a prognostic factor were retrospectively evaluated in 117 HER2-analysed patients.
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