scholarly journals Prediction of High-Risk Group of Primary Refractory Diffuse Large B-Cell Lymphoma (DLBCL) Patients Using a CT-Based Radiomics Model with Machine Learning

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4136-4136 ◽  
Author(s):  
Raoul Santiago ◽  
Johanna Ortiz Jimenez ◽  
Reza Forghani ◽  
Nikesh Muthukrishnan ◽  
Olivier Del Corpo ◽  
...  
Keyword(s):  
Machine Learning ◽  
B Cell ◽  
Prediction Models ◽  
A Priori ◽  
B Cell Lymphoma ◽  
Superior Performance ◽  
Support Vector ◽  
Large B Cell ◽  
Mean Sensitivity ◽  
Testing Set

Introduction Approximately 15% of diffuse large B-cell lymphomas (DLBCL) do not respond to R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone) or equivalent regimen. These primary refractory cases (prDLBCL) have a particularly poor survival. There are currently no reliable biomarkers to a priori identify prDLBCL patients and include them in clinical trials, while avoiding needless toxicity from predictably ineffective therapy. In this study, we evaluated the potential for radiomic analysis with machine learning for predicting prDLBCL. Method This study included adult patients with prDLBCL from a single institution from 2009 to 2018, who had first-line treatment with an R-CHOP like regimen, had never received systemic treatment for indolent lymphoma, and who had a CT scan at the time of diagnosis. Refractory (R) patients were defined by progression of disease (PD) after completion of at least one cycle, or failure to achieve a complete response (CR) after at least 4 cycles, as per Lugano criteria (Cheson, JCO 2014). Non-refractory (NR) patients were matched 1:1 on sex and R-IPI for the comparison group. Enlarged lymph nodes (≥1.5 cm in greatest diameter) were eligible for evaluation. The 6 largest nodes were selected at each node site (abdomen, chest, axilla and neck) and for each node category (refractory node (RN), partial response (PR) and CR, as per Lugano criteria). 3D Slicer software was used for the delineation of the region of interest (ROI) either for subsequent 2D analysis (largest axial section) or 3D analysis (total node volume). Each node was manually contoured by two independent readers and also was reviewed by an experienced senior oncologic radiologist. A total of 788 and 1218 features were extracted from 2D and 3D regions of interest, respectively, using Pyradiomics open source software. Two independent machine learning approaches, Random Forests (RF) and Support Vector Machine (SVM), were tested for constructing the prediction models. 70% of cases were randomly assigned to the training set and 30% to the independent testing set. In the node model (NM) each independent node's response to treatment was predicted. In the patient model (PM), groups of nodes per site (abdomen, chest, axilla and neck) were used to predict the overall patient response. Results A total of 26 refractory patients were identified with a total 149 nodes (RN=55, PR=20, CR=74) and matched to 26 NR patients for comparison, with a total of 105 CR nodes. Seventeen nodes with significant artifact were excluded from the analysis (7 from NR patients and 10 from R patients). RF had consistently superior performance compared to SVM and was used for constructing the final prediction models. Furthermore, 2D radiomic analysis had superior performance compared to 3D radiomic analysis. In the independent testing (prediction) set, the mean accuracy between the 2 readers for this model for distinguishing a R from NR patient was 80% (mean sensitivity and specificity, 73% and 88%, respectively). This model was able to predict a R patient (positive predictive value (PPV)) in 100% and 71% of the case, respectively for readers 1 and 2. The area under the ROC curve (AUC) was 0.96 and 0.81 for reader 1 and 2, respectively (Figure 1A). For performance of the radiomic model for distinguishing individual refractory from responsive nodes, the independent testing set had a mean accuracy of 75% (mean sensitivity, specificity, PPV, and NPV of 80%, 69%, 78%, and 71% respectively). The AUC per reader were 0.82 and 0.85 (Figure 1B). Conclusion We demonstrate that the use of CT radiomic analysis with machine learning for identifying a priori primary refractory DLBCL patients is feasible. These models provide a relatively high prediction accuracy, which currently cannot be done in the clinical setting based on standard, largely qualitative, imaging characteristics. The main limitations of our study include small patient numbers in this pilot study and exclusion of extranodal sites. The next step for this project would be to evaluate this approach in a larger cohort that includes a second independent institution. CT-based radiomics is promising and should be further explored to achieve this unmet need for predicting prDLBCL prior to therapy initiation. Disclosures Forghani: GE Healthcare: Consultancy, Honoraria, Research Funding; 4Intel Inc: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Other: Founder. Reinhold:FRQS: Other: FRQS Grant. Assouline:Pfizer: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Abbvie: Consultancy, Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria.

scholarly journals Probability calibration-based prediction of recurrence rate in patients with diffuse large B-cell lymphoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Shuanglong Fan ◽  
Zhiqiang Zhao ◽  
Yanbo Zhang ◽  
Hongmei Yu ◽  
Chuchu Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
B Cell ◽  
Recurrence Rate ◽  
Cell Lymphoma ◽  
Learning Algorithms ◽  
B Cell Lymphoma ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Background Although many patients receive good prognoses with standard therapy, 30–50% of diffuse large B-cell lymphoma (DLBCL) cases may relapse after treatment. Statistical or computational intelligent models are powerful tools for assessing prognoses; however, many cannot generate accurate risk (probability) estimates. Thus, probability calibration-based versions of traditional machine learning algorithms are developed in this paper to predict the risk of relapse in patients with DLBCL. Methods Five machine learning algorithms were assessed, namely, naïve Bayes (NB), logistic regression (LR), random forest (RF), support vector machine (SVM) and feedforward neural network (FFNN), and three methods were used to develop probability calibration-based versions of each of the above algorithms, namely, Platt scaling (Platt), isotonic regression (IsoReg) and shape-restricted polynomial regression (RPR). Performance comparisons were based on the average results of the stratified hold-out test, which was repeated 500 times. We used the AUC to evaluate the discrimination ability (i.e., classification ability) of the model and assessed the model calibration (i.e., risk prediction accuracy) using the H-L goodness-of-fit test, ECE, MCE and BS. Results Sex, stage, IPI, KPS, GCB, CD10 and rituximab were significant factors predicting the 3-year recurrence rate of patients with DLBCL. For the 5 uncalibrated algorithms, the LR (ECE = 8.517, MCE = 20.100, BS = 0.188) and FFNN (ECE = 8.238, MCE = 20.150, BS = 0.184) models were well-calibrated. The errors of the initial risk estimate of the NB (ECE = 15.711, MCE = 34.350, BS = 0.212), RF (ECE = 12.740, MCE = 27.200, BS = 0.201) and SVM (ECE = 9.872, MCE = 23.800, BS = 0.194) models were large. With probability calibration, the biased NB, RF and SVM models were well-corrected. The calibration errors of the LR and FFNN models were not further improved regardless of the probability calibration method. Among the 3 calibration methods, RPR achieved the best calibration for both the RF and SVM models. The power of IsoReg was not obvious for the NB, RF or SVM models. Conclusions Although these algorithms all have good classification ability, several cannot generate accurate risk estimates. Probability calibration is an effective method of improving the accuracy of these poorly calibrated algorithms. Our risk model of DLBCL demonstrates good discrimination and calibration ability and has the potential to help clinicians make optimal therapeutic decisions to achieve precision medicine.

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

scholarly journals CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma

2021 ◽  
Vol 14 (10) ◽  
pp. 101188
Author(s):  
Raoul Santiago ◽  
Johanna Ortiz Jimenez ◽  
Reza Forghani ◽  
Nikesh Muthukrishnan ◽  
Olivier Del Corpo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Treatment Failure ◽  
B Cell ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Primary Treatment ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Performance of machine learning-augmented analysis of radiomics for the head and neck cancer histopathological diagnosis: A systematic review and meta-analysis.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e18526-e18526
Author(s):  
Sotirios Bisdas ◽  
Jade Seguin ◽  
Diana Roettger ◽  
Daisuke Yoneoka ◽  
Faiq Shaikh
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Head And Neck ◽  
Meta Analysis ◽  
Superior Performance ◽  
Support Vector ◽  
Histopathological Diagnosis ◽  
Weighted Mean ◽  
Number Of Patients ◽  
Testing Set

e18526 Background: The imaging criteria used for head and neck cancers (HNC) staging are mostly anatomical with basic quantitative measures, such as size, and admittedly radiologists’ reading of images is dependent on their expertise level. Radiomics, a term referring to extracting and investigating higher dimensional data from images, has been suggested to address these shortcomings. Assisted by machine learning (ML), highly efficient prediction models could revolutionise our diagnostic practices. Our goal was to study the role of ML in the histopathological diagnosis of HNC based on radiomics. Methods: A systematic review and meta-analysis was conducted using electronic databases (PubMed, Scopus, EMBASE, Google Scholar) and including MRI, PET, and CT studies in patients with HNC. Our study was aimed only at diagnosis utilising radiomics and artificial intelligence (ML). A PRISMA diagram retracing the steps of this search process was completed. QUADAS-2 and EQUATOR checklists were completed. A weighted mean, a mean and a median of the performance indicators were recorded. Results: 7 studies were found eligible for meta-analysis. Patient sample sizes ranged between 2-107 patients (median: 18). CT was the most common modality used (4/7 studies). All but one studies were retrospective. Support vector machine and random forest techniques were the main ML techniques used but how the model was built was rarely described. Furthermore, studies did not make clear the exact number of patients in the testing set. Other issues included the reporting of the final model performance with few studies reporting confidence intervals and 2 studies not reporting the exact performance metrics. The accuracy values for the testing set ranged from 58% -94.1%. The meta-analysis showed an overall weighted-mean accuracy of 78.53%, a mean of 82.9% and a median of 84.4%. The weighted mean of the sensitivity was 76.5%, the mean was 83.3%, and for specificity was 83.9% and 88.5%., respectively. The AUC was 0.8. The neuroradiologists’ overall accuracy was 50.4% if weighted, and 54.5% if not, and the corresponding accuracy of the ML classifiers were 78.4% and 79.6%. The ML scored an accuracy of 20% higher than the radiologists. Conclusions: The results are overall encouraging, keeping in perspective the possible calculation biases and small number of studies. There is need for better documentation and standardisation of the applied ML models, which show initially superior performance compared to radiologists.

scholarly journals Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles

2015 ◽  
Vol 14 (11) ◽  
pp. 2947-2960 ◽  
Author(s):  
Sally J. Deeb ◽  
Stefka Tyanova ◽  
Michael Hummel ◽  
Marc Schmidt-Supprian ◽  
Juergen Cox ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Expression ◽  
B Cell ◽  
Cell Lymphoma ◽  
Expression Profiles ◽  
B Cell Lymphoma ◽  
Large B Cell Lymphoma ◽  
Protein Expression Profiles ◽  
Large B Cell

scholarly journals Prediction of Type 2 Diabetes Based on Machine Learning Algorithm

2021 ◽  
Vol 18 (6) ◽  
pp. 3317
Author(s):  
Henock M. Deberneh ◽  
Intaek Kim
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Prediction Model ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Recursive Feature Elimination ◽  
Medical Institute ◽  
Support Vector

Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year (Y). The dataset for this study was collected at a private medical institute as electronic health records from 2013 to 2018. To construct the prediction model, key features were first selected using ANOVA tests, chi-squared tests, and recursive feature elimination methods. The resultant features were fasting plasma glucose (FPG), HbA1c, triglycerides, BMI, gamma-GTP, age, uric acid, sex, smoking, drinking, physical activity, and family history. We then employed logistic regression, random forest, support vector machine, XGBoost, and ensemble machine learning algorithms based on these variables to predict the outcome as normal (non-diabetic), prediabetes, or diabetes. Based on the experimental results, the performance of the prediction model proved to be reasonably good at forecasting the occurrence of T2D in the Korean population. The model can provide clinicians and patients with valuable predictive information on the likelihood of developing T2D. The cross-validation (CV) results showed that the ensemble models had a superior performance to that of the single models. The CV performance of the prediction models was improved by incorporating more medical history from the dataset.

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