scholarly journals Radiomics-based prediction for tumour spread through air spaces in stage I lung adenocarcinoma using machine learning

2020 ◽  
Vol 58 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Donglai Chen ◽  
Yunlang She ◽  
Tingting Wang ◽  
Huikang Xie ◽  
Jian Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Naive Bayes ◽  
External Validation ◽  
Area Under The Curve ◽  
Naïve Bayes ◽  
Stage I ◽  
Receiver Operating Curve ◽  
Tumour Spread ◽  
Spread Through Air Spaces

Abstract OBJECTIVES As evidence has proven that sublobar resection is oncologically contraindicated by tumour spread through air spaces (STAS), its preoperative recognition is vital in customizing surgical strategies. We aimed to assess the value of radiomics in predicting STAS in stage I lung adenocarcinoma. METHODS We retrospectively reviewed the patients with stage I lung adenocarcinoma, who accepted curative resection in our institution between January 2011 and December 2013. Using ‘PyRadiomics’ package, 88 radiomics features were extracted from computed tomography (CT) images and a prediction model was consequently constructed using Naïve Bayes machine-learning approach. The accuracy of the model was assessed through receiver operating curve analysis, and the performance of the model was validated both internally and externally. RESULTS A total of 233 patients were included as the training cohort with 69 (29.6%) patients being STAS (+). Patients with STAS had worse recurrence-free survival and overall survival (P < 0.001). After feature extraction, 5 most contributing radiomics features were selected out to develop a Naïve Bayes model. In the internal validation, the model exhibited good performance with an area under the curve value of 0.63 (0.55–0.71). External validation was conducted on a test cohort with 112 patients and produced an area under the curve value of 0.69. CONCLUSIONS CT-based radiomics is valuable in preoperatively predicting STAS in stage I lung adenocarcinoma, which may aid surgeons in determining the optimal surgical approach.

scholarly journals Clinical implication of tumour spread through air spaces in pathological stage I lung adenocarcinoma treated with lobectomy

2020 ◽  
Vol 32 (1) ◽  
pp. 64-72
Author(s):  
Eunjue Yi ◽  
Jeong Hyeon Lee ◽  
Younggi Jung ◽  
Jae Ho Chung ◽  
Youngseok Lee ◽  
...  
Keyword(s):  
Risk Factor ◽  
Lung Adenocarcinoma ◽  
Survival Curve ◽  
Multivariable Analysis ◽  
Stage I ◽  
Pulmonary Lobectomy ◽  
Pathological Stage ◽  
Tumour Spread ◽  
Risk Factor For Recurrence ◽  
Spread Through Air Spaces

Abstract OBJECTIVES The aim of this study was to evaluate the clinical implication of tumour spread through air spaces (STAS) as a prognostic factor in pathological stage I lung adenocarcinoma treated with lobectomy and to identify related parameters. METHODS Medical records of patients who underwent pulmonary lobectomy for stage I (American Joint Committee on Cancers eighth edition) lung adenocarcinomas between 2012 and February 2018 at our institutions were reviewed retrospectively. Patients with minimally invasive adenocarcinomas and tumours ≥3 cm in size were excluded. Included patients were classified into STAS (+) and STAS (−) groups. Clinical implications of STAS and recurrence in patients were investigated. RESULTS A total of 109 patients was analysed: 41 (37.6%) in the STAS (+) and 68 (62.4%) in the STAS (−) group. STAS was associated with larger consolidation diameter on chest tomography (≥1.5 cm; P = 0.006) or a higher invasive ratio (≥85%; P = 0.012) and presence of a micropapillary pattern in multivariable analysis (P = 0.003) The recurrence-free survival curve showed statistical difference (P = 0.008) with 3-year survival rates of 73.0% (9 patients) and 96.8% (2 patients) in the STAS (+) and STAS (−) group, respectively. However, no statistical significance was observed in the lung cancer-related survival curve (P = 0.648). The presence of STAS was an independent risk factor for recurrence in multivariable analysis (hazard ratio = 5.9, P = 0.031). CONCLUSIONS The presence of STAS could be an important risk factor for recurrence in patients with early-stage invasive lung adenocarcinoma treated with pulmonary lobectomy.

scholarly journals Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

2020 ◽  
Vol 17 (18) ◽  
pp. 6449
Author(s):  
Parastoo Golpour ◽  
Majid Ghayour-Mobarhan ◽  
Azadeh Saki ◽  
Habibollah Esmaily ◽  
Ali Taghipour ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Coronary Angiography ◽  
Naive Bayes ◽  
Area Under The Curve ◽  
Naïve Bayes ◽  
Support Vector ◽  
Bayes Model

(1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an appropriate method to be applied to determine the necessity for angiography. The objective of this study was to compare support vector machine, naïve Bayes and logistic regressions to determine the diagnostic factors that can predict the need for coronary angiography. These models are machine learning algorithms. Machine learning is considered to be a branch of artificial intelligence. Its aims are to design and develop algorithms that allow computers to improve their performance on data analysis and decision making. The process involves the analysis of past experiences to find practical and helpful regularities and patterns, which may also be overlooked by a human. (2) Materials and Methods: This cross-sectional study was performed on 1187 candidates for angiography referred to Ghaem Hospital, Mashhad, Iran from 2011 to 2012. A logistic regression, naive Bayes and support vector machine were applied to determine whether they could predict the results of angiography. Afterwards, the sensitivity, specificity, positive and negative predictive values, AUC (area under the curve) and accuracy of all three models were computed in order to compare them. All analyses were performed using R 3.4.3 software (R Core Team; Auckland, New Zealand) with the help of other software packages including receiver operating characteristic (ROC), caret, e1071 and rminer. (3) Results: The area under the curve for logistic regression, naïve Bayes and support vector machine were similar—0.76, 0.74 and 0.75, respectively. Thus, in terms of the model parsimony and simplicity of application, the naïve Bayes model with three variables had the best performance in comparison with the logistic regression model with seven variables and support vector machine with six variables. (4) Conclusions: Gender, age and fasting blood glucose (FBG) were found to be the most important factors to predict the result of coronary angiography. The naïve Bayes model performed well using these three variables alone, and they are considered important variables for the other two models as well. According to an acceptable prediction of the models, they can be used as pragmatic, cost-effective and valuable methods that support physicians in decision making.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

scholarly journals SURG-02. SURVIVAL PREDICTION AFTER NEUROSURGICAL RESECTION OF BRAIN METASTASES: A MACHINE LEARNING APPROACH

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii203-ii203
Author(s):  
Alexander Hulsbergen ◽  
Yu Tung Lo ◽  
Vasileios Kavouridis ◽  
John Phillips ◽  
Timothy Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Metastases ◽  
External Validation ◽  
Superior Performance ◽  
Prognostic Models ◽  
Receiver Operating Curve ◽  
Gradient Boosting ◽  
Survival Prediction ◽  
Ensemble Model ◽  
Adaptive Boosting

Abstract INTRODUCTION Survival prediction in brain metastases (BMs) remains challenging. Current prognostic models have been created and validated almost completely with data from patients receiving radiotherapy only, leaving uncertainty about surgical patients. Therefore, the aim of this study was to build and validate a model predicting 6-month survival after BM resection using different machine learning (ML) algorithms. METHODS An institutional database of 1062 patients who underwent resection for BM was split into a 80:20 training and testing set. Seven different ML algorithms were trained and assessed for performance. Moreover, an ensemble model was created incorporating random forest, adaptive boosting, gradient boosting, and logistic regression algorithms. Five-fold cross validation was used for hyperparameter tuning. Model performance was assessed using area under the receiver-operating curve (AUC) and calibration and was compared against the diagnosis-specific graded prognostic assessment (ds-GPA); the most established prognostic model in BMs. RESULTS The ensemble model showed superior performance with an AUC of 0.81 in the hold-out test set, a calibration slope of 1.14, and a calibration intercept of -0.08, outperforming the ds-GPA (AUC 0.68). Patients were stratified into high-, medium- and low-risk groups for death at 6 months; these strata strongly predicted both 6-months and longitudinal overall survival (p < 0.001). CONCLUSIONS We developed and internally validated an ensemble ML model that accurately predicts 6-month survival after neurosurgical resection for BM, outperforms the most established model in the literature, and allows for meaningful risk stratification. Future efforts should focus on external validation of our model.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

Significance of Spread Through Air Spaces in Resected Pathological Stage I Lung Adenocarcinoma

2018 ◽  
Vol 105 (6) ◽  
pp. 1655-1663 ◽  
Author(s):  
Gouji Toyokawa ◽  
Yuichi Yamada ◽  
Tetsuzo Tagawa ◽  
Yuka Kozuma ◽  
Taichi Matsubara ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Stage I ◽  
Pathological Stage ◽  
Spread Through Air Spaces

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals دراسة وتحليل مرضى Covid-19 باستخدام طرق التعلم الآلي

2021 ◽  
Vol 10 (1) ◽  
pp. 37-47
Author(s):  
ياسر الجناحي ياسر الجناحي
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Ensemble Method ◽  
Boosted Tree

. أنظمة التعلم الآلي (Machine Learning) في الرعاية الصحية تستخدم للتعرف على الأمراض وتشخيصها باستخدام بيانات المريض. وقد أدى استخدام أنظمة التعلم الآلي في التكنولوجيا إلى إصلاح وتحسين الرعاية الصحية، من خلال الكشف التلقائي عن الأمراض وتشخيصها، والتي بدورها تحسن صحة المريض وتنقذ الأرواح. لذلك، في هذه الدراسة، تم استخدام خوارزميات التعلم الآلي للتنبؤ بوفاة المرضى وتعافيهم. وباستخدام عدة خوارزميات سيتم توقع وفاة أو تعافي المرضى. وقد أعطت خوارزميات الـ Naïve Bayes و Bagged Trees أفضل معدلات أداء بنسبة 79? و 77? على التوالي. ومع ذلك، من حيث الدقة، أظهرت خوارزميات تصنيف الشجرة المتوسطة (MediumTree)(ensemble method Boosted Tree) والشجرة المجموعة المعززة دقة 89?. وأخيرًا أظهرت هذه الدراسة أن استخدام تقنية التعلم الآلي يمكن أن تنبه مقدمي الرعاية الصحية لتقديم علاج أسرع لمرضى فيروس كورونا عالي الخطورة (COVID-19) مما يساعد في إنقاذ الأرواح وتحسن جودة خدمة الرعاية الصحية.

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