Background/Aim: Accurate prediction of radiotherapy results is indispensable for the individualized selection of treatment modalities of cancer. We examined the application of the artificial neural network (ANN) model in predicting radiotherapy results using clinical factors and immunohistochemical staining of Ku70 as inputs. Patients and Methods: We analyzed 79 prostate cancer patients with localized adenocarcinoma treated with radiotherapy between August 2001 and October 2010. We also analyzed 46 hypopharyngeal cancer patients with squamous cell carcinoma treated with radiotherapy between March 2002 and December 2009. The properly trained ANN analysis using a standard feedforward, back-propagation neural network was used to predict the radiotherapy treatment results. Results: The areas under the receiver-operating characteristic curve (AUC) were 0.939 for patients treated with intensity modulated radiotherapy (IMRT)+androgen deprivation therapy (ADT), 0.803 for IMRT alone, and 0.960 for 3D-conformal radiotherapy (CRT) alone in prostate cancer. Sensitivity and specificity were 85.7% and 90.4% for IMRT+ADT, 75.0% and 88.5% for IMRT alone, and 92.3% and 100% for 3D-CRT alone. The AUC was 0.901 for hypopharyngeal cancer. Sensitivity and specificity were 66.7% and 88.2%, respectively. Conclusion: We demonstrated a possibility to predict the radiotherapy treatment results in prostate and hypopharyngeal cancer using ANN in combination with Ku70 expression and clinical factors as inputs. Copyright
Background/Aim: Accurate prediction of radiotherapy results is indispensable for the individualized selection of treatment modalities of cancer. We examined the application of the artificial neural network (ANN) model in predicting radiotherapy results using clinical factors and immunohistochemical staining of Ku70 as inputs. Patients and Methods: We analyzed 79 prostate cancerpatients with localized adenocarcinoma treated with radiotherapy between August 2001 and October 2010. We also analyzed 46 hypopharyngeal cancerpatients with squamous cell carcinoma treated with radiotherapy between March 2002 and December 2009. The properly trained ANN analysis using a standard feedforward, back-propagation neural network was used to predict the radiotherapy treatment results. Results: The areas under the receiver-operating characteristic curve (AUC) were 0.939 for patients treated with intensity modulated radiotherapy (IMRT)+androgen deprivation therapy (ADT), 0.803 for IMRT alone, and 0.960 for 3D-conformal radiotherapy (CRT) alone in prostate cancer. Sensitivity and specificity were 85.7% and 90.4% for IMRT+ADT, 75.0% and 88.5% for IMRT alone, and 92.3% and 100% for 3D-CRT alone. The AUC was 0.901 for hypopharyngeal cancer. Sensitivity and specificity were 66.7% and 88.2%, respectively. Conclusion: We demonstrated a possibility to predict the radiotherapy treatment results in prostate and hypopharyngeal cancer using ANN in combination with Ku70 expression and clinical factors as inputs. Copyright
Authors: K Sakata; Y Matsumoto; H Tauchi; M Satoh; A Oouchi; H Nagakura; K Koito; Y Hosoi; N Suzuki; K Komatsu; M Hareyama Journal: Int J Radiat Oncol Biol Phys Date: 2001-01-01 Impact factor: 7.038
Authors: A V D'Amico; R Whittington; S B Malkowicz; D Schultz; K Blank; G A Broderick; J E Tomaszewski; A A Renshaw; I Kaplan; C J Beard; A Wein Journal: JAMA Date: 1998-09-16 Impact factor: 56.272
Authors: Sarah L Kerns; Leila Dorling; Laura Fachal; Søren Bentzen; Paul D P Pharoah; Daniel R Barnes; Antonio Gómez-Caamaño; Ana M Carballo; David P Dearnaley; Paula Peleteiro; Sarah L Gulliford; Emma Hall; Kyriaki Michailidou; Ángel Carracedo; Michael Sia; Richard Stock; Nelson N Stone; Matthew R Sydes; Jonathan P Tyrer; Shahana Ahmed; Matthew Parliament; Harry Ostrer; Barry S Rosenstein; Ana Vega; Neil G Burnet; Alison M Dunning; Gillian C Barnett; Catharine M L West Journal: EBioMedicine Date: 2016-07-20 Impact factor: 8.143