PACE: A Probabilistic Atlas for Normal Tissue Complication Estimation in Radiation Oncology

In radiation oncology, the need for a modern Normal Tissue Complication Probability (NTCP) philosophy to include voxel-based evidence on organ radio-sensitivity (RS) has been acknowledged. Here a new formalism (Probabilistic Atlas for Complication Estimation, PACE) to predict radiation-induced morbidity (RIM) is presented. The adopted strategy basically consists in keeping the structure of a classical, phenomenological NTCP model, such as the Lyman-Kutcher-Burman (LKB), and replacing the dose distribution with a collection of RIM odds, including also significant non-dosimetric covariates, as input of the model framework. The theory was first demonstrated in silico on synthetic dose maps, classified according to synthetic outcomes. PACE was then applied to a clinical dataset of thoracic cancer patients classified for lung fibrosis. LKB models were trained for comparison. Overall, the obtained learning curves showed that the PACE model outperformed the LKB and predicted synthetic outcomes with an accuracy >0.8. On the real patients, PACE performance, evaluated by both discrimination and calibration, was significantly higher than LKB. This trend was confirmed by cross-validation. Furthermore, the capability to infer the spatial pattern of underlying RS map for the analyzed RIM was successfully demonstrated, thus paving the way to new perspectives of NTCP models as learning tools.

[1]  Jan-Jakob Sonke,et al.  Local dose-effect relations for lung perfusion post stereotactic body radiotherapy. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[2]  Joseph O Deasy,et al.  A Voxel-Based Approach to Explore Local Dose Differences Associated With Radiation-Induced Lung Damage. , 2016, International journal of radiation oncology, biology, physics.

[3]  Gregory Schott,et al.  Data Analysis in High Energy Physics: A Practical Guide to Statistical Methods , 2013 .

[4]  J. Sengupta.  The Nonparametric Approach , 1989 .

[5]  Delia Ciardo,et al.  Voxel-based analysis unveils regional dose differences associated with radiation-induced morbidity in head and neck cancer patients , 2017, Scientific Reports.

[6]  Lawrence B. Marks,et al.  ALERT - Adverse Late Effects of Cancer Treatment , 2014 .

[7]  Joseph O Deasy,et al.  Spatial rectal dose/volume metrics predict patient-reported gastro-intestinal symptoms after radiotherapy for prostate cancer , 2017, Acta oncologica.

[8]  S. Bhide,et al.  Normal Tissue Complication Probability (NTCP) Modelling of Severe Acute Mucositis using a Novel Oral Mucosal Surface Organ at Risk. , 2017, Clinical oncology (Royal College of Radiologists (Great Britain)).

[9]  A. Nahum,et al.  Mechanistic simulation of normal-tissue damage in radiotherapy—implications for dose–volume analyses , 2010, Physics in medicine and biology.

[10]  A Brahme,et al.  Tumour and normal tissue responses to fractionated non-uniform dose delivery. , 1992, International journal of radiation biology.

[11]  A. van der Schaaf,et al.  Embracing phenomenological approaches to normal tissue complication probability modeling: a question of method. , 2015, International journal of radiation oncology, biology, physics.

[12]  Steve Webb,et al.  Using dose-surface maps to predict radiation-induced rectal bleeding: a neural network approach , 2009, Physics in medicine and biology.

[13]  Serena Monti,et al.  OC-0512: Space based normal tissue complication probability modeling , 2018 .

[14]  C. Fiorino,et al.  First application of a pixel-wise analysis on bladder dose-surface maps in prostate cancer radiotherapy. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[15]  E. Yorke,et al.  Use of normal tissue complication probability models in the clinic. , 2010, International journal of radiation oncology, biology, physics.

[16]  Raffaele Liuzzi,et al.  Modeling the risk of radiation-induced lung fibrosis: Irradiated heart tissue is as important as irradiated lung. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[17]  Jung Hun Oh,et al.  Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy , 2017, Clinical and translational radiation oncology.

[18]  A. Mauron,et al.  A question of method , 2008, EMBO reports.

[19]  O. Acosta,et al.  Voxel-based population analysis for correlating local dose and rectal toxicity in prostate cancer radiotherapy , 2013, Physics in medicine and biology.

[20]  Pascal Haigron,et al.  Identification of a rectal subregion highly predictive of rectal bleeding in prostate cancer IMRT. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[21]  Martin A. Ebert,et al.  Modeling Urinary Dysfunction After External Beam Radiation Therapy of the Prostate Using Bladder Dose-Surface Maps: Evidence of Spatially Variable Response of the Bladder Surface. , 2017, International journal of radiation oncology, biology, physics.

[22]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[23]  Ulrike Schick,et al.  Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[24]  Giuseppe Palma,et al.  Inter-patient image registration algorithms to disentangle regional dose bioeffects , 2018, Scientific Reports.

[25]  Andrea Ottolenghi,et al.  Biological mechanisms of normal tissue damage: importance for the design of NTCP models. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[26]  A. Jemal,et al.  Cancer treatment and survivorship statistics, 2014 , 2014, CA: a cancer journal for clinicians.

[27]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[28]  R. Mohan,et al.  Histogram reduction method for calculating complication probabilities for three-dimensional treatment planning evaluations. , 1991, International journal of radiation oncology, biology, physics.

[29]  David A Jaffray,et al.  Voxel-by-voxel correlation between radiologically radiation induced lung injury and dose after image-guided, intensity modulated radiotherapy for lung tumors. , 2017, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[30]  Surendra M. Gupta,et al.  A Neural Network Approach , 2020 .

[31]  Joseph O. Deasy,et al.  Complication Probability Models for Radiation-Induced Heart Valvular Dysfunction: Do Heart-Lung Interactions Play a Role? , 2014, PloS one.

[32]  J. Deasy,et al.  Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters. , 2006, International journal of radiation oncology, biology, physics.

[33]  Wouter van Elmpt,et al.  Regional variability in radiation-induced lung damage can be predicted by baseline CT numbers. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.