Assessing rectal cancer treatment response using photoacoustic microscopy: deep learning CNN outperforms supervised machine learning model

2021 ◽  
Author(s):  
Xiandong Leng ◽  
Eghbal Amidi ◽  
K. M. Shihab Uddin ◽  
William C. Chapman ◽  
Hongbo Luo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rectal Cancer ◽  
Deep Learning ◽  
Cancer Treatment ◽  
Treatment Response ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Photoacoustic Microscopy ◽  
Machine Learning Model ◽  
Rectal Cancer Treatment

scholarly journals MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrea Delli Pizzi ◽  
Antonio Maria Chiarelli ◽  
Piero Chiacchiaretta ◽  
Martina d’Annibale ◽  
Pierpaolo Croce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rectal Cancer ◽  
Treatment Response ◽  
Randomized Clinical Trials ◽  
Locally Advanced ◽  
Neoadjuvant Treatment ◽  
Learning Model ◽  
Partial Least Square ◽  
Machine Learning Model ◽  
Pre Treatment

AbstractNeoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10–5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

scholarly journals From the Core to the Border of Locally Advanced Rectal Cancer: A Novel MRI-based Clinical-Radiomic Model Early Predicts Treatment Response

2020 ◽  
Author(s):  
Andrea Delli Pizzi ◽  
Antonio Chiarelli ◽  
Piero Chiacchiaretta ◽  
Martina d'Annibale ◽  
Pierpaolo Croce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rectal Cancer ◽  
Treatment Response ◽  
Randomized Clinical Trials ◽  
Locally Advanced ◽  
Neoadjuvant Treatment ◽  
Learning Model ◽  
Partial Least Square ◽  
Machine Learning Model ◽  
Pre Treatment

Abstract Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (³ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the “tumor core” (TC) and the “tumor border” (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based “clinical-radiomic” machine learning model properly predicted the treatment response (AUC=0.793, p =5.6·10-5). Importantly, the prediction improved when combining MRI-based clinical features and radiomic features, the latter extracted from both TC and TB. Prospective validation studies in randomized clinical trials are warranted to better define the role of radiomics in the development of rectal cancer precision medicine.

scholarly journals Development of a Cable Damage Detection Deep Learning Method based on Acceleration Response of Cable-Stayed Bridge

2021 ◽  
Vol 12 (6) ◽  
pp. 638-647
Author(s):  
Park Gi-Hun Et.al
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Damage Detection ◽  
Learning Model ◽  
Learning Method ◽  
Acceleration Response ◽  
Damage Location ◽  
Cable Stayed Bridge ◽  
Machine Learning Model ◽  
Cable Damage

The purpose of this thesis was to select a cable-stayed bridge to which external force may cause damage as the subject, to develop a damage detection deep learning method capable of detecting cable damage, and to test and verify the developed damage detection deep learning method. The damage detection method was developed as a system that utilizes the acceleration response of a structure measured for maintenance purposes. To extract information capable of identifying the damage locations from among the measured acceleration responses, a CNN ID was used to develop the damage detection deep learning method. The developed damage detection deep learning method was developed in a way not independently arranging 1 machine learning model per each measuring point and finally predicting the damage location based on the decision-making results collected from each machine learning model. The developed damage detection deep learning method performed the learning per each machine learning model by utilizing the acceleration response of a structure acquired based on the preliminary damage test. Finally, the damage detection deep learning method that completed the learning verified the cable damage location detection performance by utilizing the data acquired based on the cable-stayed bridge damage test. As a result, it was confirmed that the developed damage detection deep learning method predicted the damage location of a cable-stayed bridge at an average accuracy of 89%. In the current research, only the cable-stayed bridge of the Seohaegyo Bridge was studied, but in the improved study, the research will be conducted on other bridges and damage assessment will be conducted on all cables.

scholarly journals Quantitative Toxicity Prediction via Ensembling of Heterogeneous Predictors

2019 ◽  
Author(s):  
Abdul Karim ◽  
Vahid Riahi ◽  
Avinash Mishra ◽  
Abdollah Dehzangi ◽  
M. A. Hakim Newton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Models ◽  
Individual Performance ◽  
Learning Model ◽  
Data Representation ◽  
Toxicity Prediction ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Benchmark Datasets

Abstract Representing molecules in the form of only one type of features and using those features to predict their activities is one of the most important approaches for machine-learning-based chemical-activity-prediction. For molecular activities like quantitative toxicity prediction, the performance depends on the type of features extracted and the machine learning approach used. For such cases, using one type of features and machine learning model restricts the prediction performance to specific representation and model used. In this paper, we study quantitative toxicity prediction and propose a machine learning model for the same. Our model uses an ensemble of heterogeneous predictors instead of typically using homogeneous predictors. The predictors that we use vary either on the type of features used or on the deep learning architecture employed. Each of these predictors presumably has its own strengths and weaknesses in terms of toxicity prediction. Our motivation is to make a combined model that utilizes different types of features and architectures to obtain better collective performance that could go beyond the performance of each individual predictor. We use six predictors in our model and test the model on four standard quantitative toxicity benchmark datasets. Experimental results show that our model outperforms the state-of-the-art toxicity prediction models in 8 out of 12 accuracy measures. Our experiments show that ensembling heterogeneous predictor improves the performance over single predictors and homogeneous ensembling of single predictors.The results show that each data representation or deep learning based predictor has its own strengths and weaknesses, thus employing a model ensembling multiple heterogeneous predictors could go beyond individual performance of each data representation or each predictor type.

scholarly journals Translationese and Post-editese: How comparable is comparable quality?

2018 ◽  
Vol 16 ◽  
Author(s):  
Joke Daems ◽  
Orphée De Clercq ◽  
Lieve Macken
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Original Text ◽  
High Quality ◽  
Machine Learning Model ◽  
Comparable Quality

Whereas post-edited texts have been shown to be either of comparable quality to human translations or better, one study shows that people still seem to prefer human-translated texts. The idea of texts being inherently different despite being of high quality is not new. Translated texts, for example, are also different from original texts, a phenomenon referred to as ‘Translationese’. Research into Translationese has shown that, whereas humans cannot distinguish between translated and original text, computers have been trained to detect Translationese successfully. It remains to be seen whether the same can be done for what we call Post-editese. We first establish whether humans are capable of distinguishing post-edited texts from human translations, and then establish whether it is possible to build a supervised machine-learning model that can distinguish between translated and post-edited text.

scholarly journals Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

2019 ◽  
Vol 8 (6) ◽  
pp. 4368-4373
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Network Approaches

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.

c-Cbl expression as a novel predictive marker of survival in patients with metastatic colorectal cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15090-e15090
Author(s):  
Shin Yin Lee ◽  
Vijaya B. Kolachalama ◽  
Umit Tapan ◽  
Janice Weinberg ◽  
Jean M. Francis ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Medical Center ◽  
Area Under The Curve ◽  
Predictive Marker ◽  
Learning Model ◽  
Negative Regulator ◽  
Supervised Machine Learning ◽  
Molecular Features ◽  
Machine Learning Model

e15090 Background: Aberrant hyperactive Wnt/ß-catenin signaling is critical in colorectal cancer (CRC) tumorigenesis. Casitas B-lineage Lymphoma (c-Cbl) is a negative regulator of Wnt signaling, and functions as a tumor suppressor. The objective of this study was to evaluate c-Cbl expression as a predictive marker of survival in patients with metastatic CRC (mCRC). Methods: Patients with mCRC treated at Boston University Medical Center between 2004 and 2014 were analyzed. c-Cbl and nuclear ß-catenin expression was quantified in explanted biopsies using a customized color-based image segmentation pipeline. Quantification was normalized to the total tumor area in an image, and deemed ‘low’ or ‘high’ according to the mean normalized values of the cohort. A supervised machine-learning model based on bootstrap aggregating was constructed with c-Cbl expression as the input feature and 3-year survival as output. Results: Of the 72 subjects with mCRC, 52.78% had high and 47.22% had low c-Cbl expression. Patients with high c-Cbl had significantly better median overall survival than those with low c-Cbl expression (3.7 years vs. 1.8 years; p = 0.0026), and experienced superior 3-year survival (47.37% vs 20.59%; p = 0.017). Intriguingly, nuclear ß-catenin expression did not correlate with survival. No significant differences were detected between high and low c-Cbl groups in baseline characteristics (demographics, comorbidities), tumor-related parameters (primary tumor location, number of metastasis, molecular features) or therapy received (surgery, chemotherapy regimen). A 5-fold cross-validated machine-learning model associated with 3-year survival demonstrated an area under the curve of 0.729, supporting c-Cbl expression as a predictor of mCRC survival. Conclusions: Our results show that c-Cbl expression is associated with and predicts mCRC survival. Demonstration of these findings despite the small cohort size underscores the power of quantitative histology and machine-learning application. While further work is needed to validate c-Cbl as a novel biomarker of mCRC survival, this study supports c-Cbl as a regulator of Wnt/ß-catenin signaling and a suppressor of other oncogenes in CRC tumorigenesis.

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