scholarly journals Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer

2021 ◽  
Vol 94 (1120) ◽  
pp. 20200026
Author(s):  
Laia Humbert-Vidan ◽  
Vinod Patel ◽  
Ilkay Oksuz ◽  
Andrew Peter King ◽  
Teresa Guerrero Urbano
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Support Vector ◽  
Ann Model ◽  
Adaptive Boosting ◽  
Significant Difference

Objectives: Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetric factors. This study proposes the use of machine learning (ML) methods as prediction models for mandible ORN incidence. Methods: A total of 96 patients (ORN incidence ratio of 1:1) treated between 2011 and 2015 were selected from the local HNC toxicity database. Demographic, clinical and dosimetric data (based on the mandible dose–volume histogram) were considered as model variables. Prediction accuracy (measured using a stratified fivefold nested cross-validation), sensitivity, specificity, precision and negative predictive value were used to evaluate the prediction performance of a multivariate logistic regression (LR) model, a support vector machine (SVM) model, a random forest (RF) model, an adaptive boosting (AdaBoost) model and an artificial neural network (ANN) model. The different models were compared based on their prediction accuracy and using the McNemar’s hypothesis test. Results: The ANN model (77% accuracy), closely followed by the SVM (76%), AdaBoost (75%) and LR (75%) models, showed the highest overall prediction accuracy. The RF model (71%) showed the lowest prediction accuracy. However, based on the McNemar’s test applied to all model pair combinations, no statistically significant difference between the models was found. Conclusion: Based on our results, we encourage the use of ML-based prediction models for ORN incidence as has already been done for other HNC toxicity end points. Advances in knowledge: This research opens a new path towards personalised RT for HNC using ML to predict mandible ORN incidence.

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110035
Author(s):  
Anik Das ◽  
Mohamed M. Ahmed
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lane Change ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Change Prediction

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

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 Evaluation of the prevalence and severity of xerophthalmia in head and neck cancers patients undergoing curative radiotherapy

2016 ◽  
Vol 14 (4) ◽  
Author(s):  
Sowmya V ◽  
Dipika Jayachander ◽  
Vijna Kamath ◽  
Mithun SK Rao ◽  
Mohammed Raees Tonse ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Keratoconjunctivitis Sicca ◽  
Study Objective ◽  
Cancer Group ◽  
Curative Radiotherapy ◽  
Significant Difference ◽  
Rectal Cancers

  Background: The study objective was to assess the development of xerophthalmia [dry eye syndrome (DES) or keratoconjunctivitis sicca] in head and neck cancer patients undergoing radiotherapy.Methods: Twenty two head and neck cancer patients requiring more than 60 Gy of curative radiotherapy/chemoradiotherapy and ten patients requiring radiotherapy/ chemoradiotherapy for treating cancers in the non head and neck regions (like breast, oesophagus, prostate, cervix and rectal cancers) were also enrolled in the study. The development of DES was studied at the beginning (day 0, before the start of radiotherapy) at day 21 (after completion of 30 Gy) and on completion of the treatment (> 60 Gy). As a comparative cohort, people with non head and neck cancer needing curative radiotherapy were also evaluated for comparison.Results: There was no difference in degree of DES between the Head and Neck cancer cohorts and non head and neck group at the beginning of treatment. However there was a statistically significant difference (p < 0.001) between the two groups at both mid and end of RT time point. Inter comparison between the various time points in the head and neck cancer group showed that the incidence of DES increased with the radiation exposure and was significant (pre to mid p < 0.001; and mid to end p < 0.005). A negative (r = -0.262) correlation was seen between DES and distance.Conclusions: The study showed that lesser the distance from the epicenter of the radiation to the orbital rim more was the severity of DES.

Importance of Treatment Institution in Head and Neck Cancer Radiotherapy

2009 ◽  
Vol 141 (2) ◽  
pp. 172-176 ◽  
Author(s):  
Gregory J. Kubicek ◽  
Fen Wang ◽  
Eashwar Reddy ◽  
Yelizaveta Shnayder ◽  
Cristina E. Cabrera ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Treatment Outcome ◽  
Head And Neck ◽  
Neck Cancer ◽  
Time Course ◽  
Medical Center ◽  
Recurrence Rates ◽  
Historical Cohort ◽  
Initial Surgery ◽  
Significant Difference

OBJECTIVE: The treatment for head and neck cancer (HNC) often involves radiotherapy. Many HNC patients are treated at the academic center (AC) where the initial surgery or diagnosis was made. Because of the lengthy time course for radiotherapy, some patients are treated at community radiation facilities (non-AC) rather than the AC despite potential AC advantages in terms of experience and technology. Our goal is to determine if these potential AC advantages correspond to a difference in treatment outcome. STUDY DESIGN: Historical cohort study. SETTING: University of Kansas Medical Center, Kansas City, Kansas. SUBJECTS AND METHODS: Review of records of patients with HNC cancers evaluated at the otolaryngology (ENT) department of an AC. Each patient's information and treatment characteristics were recorded, including radiotherapy treatment venue and treatment outcome. RESULTS: Three hundred seventy-four patients were analyzed, 263 were treated at an AC and 101 at a non-AC. Patients treated at a non-AC were more likely to present with earlier stage tumors, be treated with radiation alone rather than chemoradiotherapy, and be treated with adjuvant rather than primary radiotherapy. There was no difference in overall survival or recurrence rates between AC and non-AC. CONCLUSION: Patients treated at an AC are more likely to have advanced stage tumors and receive chemoradiotherapy as their primary treatment. In analyses of matching patient subsets, there was no significant difference in patient outcomes. Patients can be treated at a non-AC without affecting outcome compared with treatment at an AC.

scholarly journals TO COMPARE THE TOXICITY PROFILE OF ACCELERATED RADIOTHERAPY (SIX FRACTIONS PER WEEK) WITH CONCOMITANT CHEMO-RADIOTHERAPY IN LOCALLY ADVANCED HEAD AND NECK CANCER

2019 ◽  
Vol 3 (11) ◽  
Author(s):  
Vikrant Kaushal ◽  
Amit Rana ◽  
Manoj Gupta ◽  
Rajeev Seam ◽  
Manish Gupta
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Randomized Study ◽  
Locally Advanced ◽  
Medical Science ◽  
Hematological Toxicity ◽  
P Value ◽  
Significant Difference ◽  
The Difference

Background: Head and neck malignancies are common among males in India. The age adjusted incidence rate of head and neck cancer in India in males is 16.4/100,000 and in females it is 8.8/100,000.In All India Institute of Medical Science head and neck cancer represents 25% of all malignancies registered Methods: This prospective randomized study was conducted in the Department of Radiation Therapy & Oncology, Regional Cancer Centre, IGMC, Shimla and patients were enrolled for a period of one year, from July 2012 to July 2013.It included all the eligible, previously untreated patients of squamous cell carcinoma of Head and Neck with histologically confirmed diagnosis and no evidence of distant metastasis. The sites included were oro-pharynx, hypo-pharynx and larynx with stages III, IV A and IV B. Results: Grade 3 and grade 4 skin toxicities were higher in CRT arm but without statistically significant difference from that in ART arm. G3 & G4 mucositis was higher in the Concomitant CRT arm however the difference was not statistically significant. G2 and G3 Laryngeal Toxicities were higher in Concomitant CRT arm as compared to Accelerated arm but the difference was not statistically significant. G2 & G3 haematological toxicities were significantly (combined p value = 0.002) higher in the concomitant CRT arm (32.4%) as compared to Accelerated RT arm (2.9%). Only one patient in accelerated arm had any hematological toxicity. Conclusion: Higher peak incidence of toxicities was seen in concomitant CRT arm as compared to accelerated arm. Keywords: Toxocity, six fraction, chemoradiation, Local control

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