Image-based Data Mining to Probe Dosimetric Correlates of Radiation-induced Trismus.

PURPOSE To identify imaged regions in which dose is associated with radiation-induced trismus after head and neck cancer radiation therapy (HNRT) using a novel image-based data mining (IBDM) framework. METHODS AND MATERIALS A cohort of 86 HNRT patients were analyzed for region identification. Trismus was characterized as a continuous variable by the maximum incisor-to-incisor opening distance (MID) at 6 months after radiation therapy. Patient anatomies and dose distributions were spatially normalized to a common frame of reference using deformable image registration. IBDM was used to identify clusters of voxels associated with MID (P ≤ .05 based on permutation testing). The result was externally tested on a cohort of 35 patients with head and neck cancer. Internally, we also performed a dose-volume histogram-based analysis by comparing the magnitude of the correlation between MID and the mean dose for the IBDM-identified cluster in comparison with 5 delineated masticatory structures. RESULTS A single cluster was identified with the IBDM approach (P < .01), partially overlapping with the ipsilateral masseter. The dose-volume histogram-based analysis confirmed that the IBDM cluster had the strongest association with MID, followed by the ipsilateral masseter and the ipsilateral medial pterygoid (Spearman's rank correlation coefficients: Rs = -0.36, -0.35, -0.32; P = .001, .001, .002, respectively). External validation confirmed an association between mean dose to the IBDM cluster and MID (Rs = -0.45; P = .007). CONCLUSIONS IBDM bypasses the common assumption that dose patterns within structures are unimportant. Our novel IBDM approach for continuous outcome variables successfully identified a cluster of voxels that are highly associated with trismus, overlapping partially with the ipsilateral masseter. Tests on an external validation cohort showed an even stronger correlation with trismus. These results support use of the region in HNRT treatment planning to potentially reduce trismus.

[1]  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.

[2]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[3]  M. Herk,et al.  Multiple comparisons permutation test for image based data mining in radiotherapy , 2013, Radiation oncology.

[4]  S. Webb,et al.  Novel approaches to improve the therapeutic index of head and neck radiotherapy: an analysis of data from the PARSPORT randomised phase III trial. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[5]  M. V. van Herk,et al.  Dose-surface maps identifying local dose-effects for acute gastrointestinal toxicity after radiotherapy for prostate cancer. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[6]  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.

[7]  Sébastien Ourselin,et al.  Fast free-form deformation using graphics processing units , 2010, Comput. Methods Programs Biomed..

[8]  J. Roodenburg,et al.  Trismus in head and neck oncology: a systematic review. , 2004, Oral oncology.

[9]  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.

[10]  S. Webb,et al.  Assessing Correlations between the Spatial Distribution of Dose to the Rectal Wall and Late Rectal Toxicity after Prostate Radiotherapy , 2009 .

[11]  Marnix G Witte,et al.  Relating dose outside the prostate with freedom from failure in the Dutch trial 68 Gy vs. 78 Gy. , 2010, International journal of radiation oncology, biology, physics.

[12]  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.

[13]  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.

[14]  M. V. van Herk,et al.  Urinary obstruction in prostate cancer patients from the Dutch trial (68 Gy vs. 78 Gy): relationships with local dose, acute effects, and baseline characteristics. , 2010, International journal of radiation oncology, biology, physics.

[15]  B. Zackrisson,et al.  Radiation-induced trismus in the ARTSCAN head and neck trial , 2014, Acta oncologica.

[16]  C. Fiorino,et al.  Bladder spatial-dose descriptors correlate with acute urinary toxicity after radiation therapy for prostate cancer. , 2016, 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.

[17]  G. Steineck,et al.  Risk structures for radiation-induced trismus in head and neck cancer , 2016, Acta oncologica.

[18]  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.

[19]  R. Munbodh,et al.  Quantifying cell migration distance as a contributing factor to the development of rectal toxicity after prostate radiotherapy. , 2014, Medical physics.

[20]  Alan Welsh,et al.  mplot: An R Package for Graphical Model Stability and Variable Selection Procedures , 2015, 1509.07583.

[21]  L. van der Molen,et al.  Dysphagia and trismus after concomitant chemo-Intensity-Modulated Radiation Therapy (chemo-IMRT) in advanced head and neck cancer; dose-effect relationships for swallowing and mastication structures. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[22]  D. Joseph,et al.  Spatial features of dose–surface maps from deformably-registered plans correlate with late gastrointestinal complications , 2017, Physics in medicine and biology.

[23]  John W. Chen,et al.  Predicting the Severity and Prognosis of Trismus after Intensity-Modulated Radiation Therapy for Oral Cancer Patients by Magnetic Resonance Imaging , 2014, PloS one.

[24]  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.

[25]  K. Rai,et al.  Trismus in oral cancer patients undergoing surgery and radiotherapy. , 2016, Journal of oral biology and craniofacial research.

[26]  T. Kehwar,et al.  Analytical approach to estimate normal tissue complication probability using best fit of normal tissue tolerance doses into the NTCP equation of the linear quadratic model. , 2005, Journal of cancer research and therapeutics.

[27]  Georg Heinze,et al.  Variable selection – A review and recommendations for the practicing statistician , 2018, Biometrical journal. Biometrische Zeitschrift.

[28]  Steve Webb,et al.  Modeling late rectal toxicities based on a parameterized representation of the 3D dose distribution , 2011, Physics in medicine and biology.

[29]  J. Deasy,et al.  Dose-volume factors correlating with trismus following chemoradiation for head and neck cancer , 2016, Acta oncologica.

[30]  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.

[31]  G. Collins,et al.  Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement , 2015, BMC Medicine.

[32]  C. Speksnijder,et al.  Maximum mouth opening and trismus in 143 patients treated for oral cancer: A 1‐year prospective study , 2014, Head & neck.

[33]  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)).

[34]  N. Pauli,et al.  Exercise intervention for the treatment of trismus in head and neck cancer , 2014, Acta oncologica.

[35]  Marcel van Herk,et al.  Radiation dose to heart base linked with poorer survival in lung cancer patients. , 2017, European journal of cancer.