A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal cancer based on PET images using radiomics and machine-learning.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 456-456
Author(s):  
Yuji Murakami ◽  
Yasushi Nagata ◽  
Daisuke Kawahara
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Predictive Model ◽  
Test Data ◽  
Locally Advanced ◽  
Neoadjuvant Chemoradiotherapy ◽  
Image Data ◽  
Ct Images ◽  
Pathological Findings ◽  
Planning Ct

456 Background: The pathologic complete response (PCR) rate by neoadjuvant chemoradiotherapy (NCRT) for resectable locally advanced esophageal squamous cell carcinoma (ESCC) is about 40%. If we could predict a PCR from pre-treatment image data, it might be possible to select patients who can be cured by organ-preserving CRT. The purpose of this study is to construct a predictive model for PCR by NCRT in patients with locally advanced ESCC using radiomics and machine-learning. Methods: We used data of 98 ESCC patients who underwent NCRT and surgery from 2003 to 2016. Firstly, we fused the radiotherapy treatment planning CT images and PET images scanned before treatment. Then using target delineations on planning CT images, we created eight kinds of target regions on PET images. Secondly, we generated a total of 6968 features per patient using the PET image data within these target regions that were preprocessed by radiomics technique. Among them, we extracted the optimal features for machine-learning using the least absolute shrinkage and selection operator (LASSO) logistic regression. Thirdly, artificial neural networks were used as a machine-learning method to create a predictive model. The extracted radiomics features were used as input values, and the information of ‘PCR’ or ‘not PCR’ was used as output values. We used data of randomly selected 58 patients for training and constructed a predictive model. Then we used data of 15 patients to validate the models and created the optimal model. Finally, we evaluated the predictive model using the test data of 25 patients. Results: By the LASSO analysis, 32 radiomics features were extracted for machine-learning classification. This predictive model predicted pathological findings after NCRT in 24 of 25 test data. The accuracy, specificity and sensitivity in the prediction of PCR after NCRT by this predictive model were 96.0%, 93.8%, and 100%, respectively. Conclusions: A prediction model based on PET images using radiomics and machine-learning could predict pathological findings after NCRT for resectable locally advanced ESCC.

A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning

2021 ◽  
pp. 20210525
Author(s):  
Daisuke Kawahara ◽  
Yuji Murakami ◽  
Shigeyuki Tani ◽  
Yasushi Nagata
Keyword(s):  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Esophageal Cancer ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Predictive Model ◽  
Squamous Cell ◽  
Locally Advanced ◽  
Degree Of Differentiation ◽  
Planning Ct

Objective: To propose the prediction model for degree of differentiation for locally advanced esophageal cancer patients from the planning CT image by radiomics analysis with machine learning. Methods: Data of 104 patients with esophagus cancer, who underwent chemoradiotherapy followed by surgery at the Hiroshima University hospital from 2003 to 2016 were analyzed. The treatment outcomes of these tumors were known prior to the study. The data were split into 3 sets: 57/16 tumors for the training/validation and 31 tumors for model testing. The degree of differentiation of squamous cell carcinoma was classified into two groups. The first group (Group I) was a poorly differentiated (POR) patients. The second group (Group II) was well and moderately differentiated patients. The radiomics feature was extracted in the tumor and around the tumor regions. A total number of 3480 radiomics features per patient image were extracted from radiotherapy planning CT scan. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors. The radiomics features were used for the input data in the machine learning. To build predictive models with radiomics features, neural network classifiers was used. The precision, accuracy, sensitivity by generating confusion matrices, the area under the curve (AUC) of receiver operating characteristic curve were evaluated. Results: By the LASSO analysis of the training data, we found 13 radiomics features from CT images for the classification. The accuracy of the prediction model was highest for using only CT radiomics features. The accuracy, specificity, and sensitivity of the predictive model were 85.4%, 88.6%, 80.0%, and the AUC was 0.92. Conclusion: The proposed predictive model showed high accuracy for the classification of the degree of the differentiation of esophagus cancer. Because of the good prediction ability of the method, the method may contribute to reducing the pathological examination by biopsy and predicting the local control. Advances in knowledge: For esophageal cancer, the differentiation of degree is the import indexes reflecting the aggressiveness. The current study proposed the prediction model for the differentiation of degree with radiomics analysis.

scholarly journals The impact of tumour regression in locally advanced carcinoma cervix during external beam radiotherapy and the need for adaptive planning

2016 ◽  
Keyword(s):  
Ct Scan ◽  
Locally Advanced ◽  
Target Volume ◽  
Ct Images ◽  
Tumour Regression ◽  
Target Coverage ◽  
Carcinoma Cervix ◽  
Advanced Carcinoma ◽  
The Impact ◽  
Planning Ct

Aim: To study the impact of tumour regression occurring during IMRT for locally advanced carcinoma cervix and study dose distribution to target volume and OARs and hence the need for any replanning. Materials and Methods: 40 patients undergoing IM-IGRT and weekly chemotherapy were included in the study. After 36 Gy, a second planning CT-scan was done and target volume and OARs were recontoured. First plan (non-adaptive) was compared with second plan (adaptive plan) to evaluate whether it would still offer sufficient target coverage to the CTV and spare the OARs after having delivered 36 Gy. Finally new plan was created based on CT-images to investigate whether creating a new treatment plan would optimize target coverage and critical organ sparing. To measure the response of the primary tumour and pathologic nodes to EBRT, the differences in the volumes of the primary GTV and nodal GTV between the pretreatment and intratreatment CT images was calculated. Second intratreatment IMRT plans was generated, using the delineations of the intratreatment CT images. The first IMRT plan (based on the first CT-scan or non adaptive plan) was compared with second IMRT plan (based on the second CT-scan or adaptive plan). Results: 35% patients had regression in GTV in the range of 4.1% to 5%, 20% in the range of 1.1%-2%, 15% in the range of 2.1%-3% and 20% in the range of 6%-15%. There was significant mean decrease in GTV of 4.63 cc (p=0.000). There was a significant decrease in CTV on repeat CT done after 36 Gy by 23.31 cc (p=0.000) and in PTV by 23.31 cc (p=0.000). There was a statistically significant increase in CTV D98, CTV D95, CTV D50 and CTV D2 in repeat planning CT done after 36 Gy. There was no significant alteration in OARs doses. Conclusion: Despite tumour regression and increased target coverage in locally advanced carcinoma cervix after a delivery of 36 Gy there was no sparing of OARs. Primary advantage of adaptive RT seems to be in greater target coverage with non-significant normal tissue sparing.

scholarly journals Building an NCAA men’s basketball predictive model and quantifying its success

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael J. Lopez ◽  
Gregory J. Matthews
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Predictive Model ◽  
Loss Function ◽  
Las Vegas ◽  
Statistical Tools ◽  
Men's Basketball ◽  
Predictive Algorithms ◽  
Complex Models ◽  
Logistic Regressions

AbstractComputing and machine learning advancements have led to the creation of many cutting-edge predictive algorithms, some of which have been demonstrated to provide more accurate forecasts than traditional statistical tools. In this manuscript, we provide evidence that the combination of modest statistical methods with informative data can meet or exceed the accuracy of more complex models when it comes to predicting the NCAA men’s basketball tournament. First, we describe a prediction model that merges the point spreads set by Las Vegas sportsbooks with possession based team efficiency metrics by using logistic regressions. The set of probabilities generated from this model most accurately predicted the 2014 tournament, relative to approximately 400 competing submissions, as judged by the log loss function. Next, we attempt to quantify the degree to which luck played a role in the success of this model by simulating tournament outcomes under different sets of true underlying game probabilities. We estimate that under the most optimistic of game probability scenarios, our entry had roughly a 12% chance of outscoring all competing submissions and just less than a 50% chance of finishing with one of the ten best scores.

scholarly journals Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Daowei Li ◽  
Qiang Zhang ◽  
Yue Tan ◽  
Xinghuo Feng ◽  
Yuanyi Yue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Laboratory Tests ◽  
Operating Characteristic ◽  
Clinical Laboratory ◽  
Severe Disease ◽  
Ct Images ◽  
Model Combining ◽  
Machine Learning Model ◽  
Machine Learning Approach

BACKGROUND Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. OBJECTIVE This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. METHODS A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients’ CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. RESULTS We present a prediction model combining patients’ radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F<sub>1</sub> score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients’ laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. CONCLUSIONS To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Machine Learning Models in Patients with Breast Cancer

2021 ◽  
Author(s):  
Ji-Yeon Kim ◽  
Eunjoo Jeon ◽  
Soonhwan Kwon ◽  
Hyungsik Jung ◽  
Sunghoon Joo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Neoadjuvant Chemotherapy ◽  
Prediction Model ◽  
Prediction Models ◽  
Locally Advanced ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Bayesian Optimization ◽  
Pathological Characteristics

Abstract BackgroundThe aim of this study was to develop a machine learning(ML) based model to accurately predict pathologic complete response(pCR) to neoadjuvant chemotherapy(NAC) using pretreatment clinical and pathological characteristics of electronic medical record(EMR) data in breast cancer(BC).Methods The EMR data from patients diagnosed with early and locally advanced BC and who received NAC followed by curative surgery were reviewed. A total of 16 clinical and pathological characteristics was selected to develop ML model. We practiced six ML models using default settings for multivariate analysis with extracted variables. ResultsIn total, 2,065 patients were included in this analysis. Overall, 30.6% (n=632) of patients achieved pCR. Among six ML models, the LightGBM had the highest area under the curve (AUC) for pCR prediction. After hyper-parameter tuning with Bayesian optimization, AUC was 0.810. Performance of pCR prediction models in different histology-based subtypes was compared. The AUC was highest in HR+HER2- subgroup and lowest in HR-/HER2- subgroup (HR+/HER2- 0.841, HR+/HER2+ 0.716, HR-/HER2 0.753, HR-/HER2- 0.653).ConclusionsA ML based pCR prediction model using pre-treatment clinical and pathological characteristics provided useful information to predict pCR during NAC. This prediction model would help to determine treatment strategy in patients with BC planned NAC.

scholarly journals Machine learning-based prediction of in-hospital mortality using admission laboratory data: A retrospective, single-site study using electronic health record data

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246640
Author(s):  
Tomohisa Seki ◽  
Yoshimasa Kawazoe ◽  
Kazuhiko Ohe
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Test Data ◽  
Random Forests ◽  
Prediction Models ◽  
Laboratory Data ◽  
Data Set ◽  
Prediction Capability

Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient’s severity. Because recent machine learning application in the clinical area has been shown to enhance prediction ability, applying this technique to this issue can lead to an accurate prediction model for in-hospital mortality prediction. In this study, we aimed to generate an accurate prediction model of in-hospital mortality using machine learning techniques. Patients 18 years of age or older admitted to the University of Tokyo Hospital between January 1, 2009 and December 26, 2017 were used in this study. The data were divided into a training/validation data set (n = 119,160) and a test data set (n = 33,970) according to the time of admission. The prediction target of the model was the in-hospital mortality within 14 days. To generate the prediction model, 25 variables (age, sex, 21 laboratory test items, length of stay, and mortality) were used to predict in-hospital mortality. Logistic regression, random forests, multilayer perceptron, and gradient boost decision trees were performed to generate the prediction models. To evaluate the prediction capability of the model, the model was tested using a test data set. Mean probabilities obtained from trained models with five-fold cross-validation were used to calculate the area under the receiver operating characteristic (AUROC) curve. In a test stage using the test data set, prediction models of in-hospital mortality within 14 days showed AUROC values of 0.936, 0.942, 0.942, and 0.938 for logistic regression, random forests, multilayer perceptron, and gradient boosting decision trees, respectively. Machine learning-based prediction of short-term in-hospital mortality using admission laboratory data showed outstanding prediction capability and, therefore, has the potential to be useful for the risk assessment of patients at the time of hospitalization.

scholarly journals Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach

10.2196/21604 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e21604
Author(s):  
Daowei Li ◽  
Qiang Zhang ◽  
Yue Tan ◽  
Xinghuo Feng ◽  
Yuanyi Yue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Laboratory Tests ◽  
Operating Characteristic ◽  
Clinical Laboratory ◽  
Severe Disease ◽  
Ct Images ◽  
Model Combining ◽  
Machine Learning Model ◽  
Machine Learning Approach

Background Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. Objective This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. Methods A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients’ CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. Results We present a prediction model combining patients’ radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients’ laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. Conclusions To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

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