Novel techniques for normal tissue toxicity modelling Laura Cella Institute of Biostructures and Bioimaging National Research Council of Italy
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4 categories of RO data : clinical data dosimetric data biological markers imaging features Radiotherapy DATA El Naqa, Methods 2016, 111:32-44 3
Radiotherapy DATA Immense potential of using a database of RO data to create more personalized treatment plans for RT patients Transform Clinical data into knowledge!!!! 4
RO data & OUTCOME modelling RO data analysis to find patterns that are clinically useful and could improve RT practice Tumor Control Probability (TCP) Normal Tissue Complication Probability (NTCP) El Naqa, IJROBP 2006, 64:1275 86 5
Radiation-induced toxicity Prediction Better evaluation of the therapeutic ratio of RT Maximum advantage from the modern technologies Translation of dosimetric benefit into clinical benefit A vehicle for a better understanding of radiotherapy response Possibility of a more realistic calculation of cost effectiveness aimed to implement new technology 6
NTCP modelling Traditionally radiotherapy outcomes have been modelled using information about the dose distribution and the fractionation For practical reasons most models start with the DVH: - loss of spatial information - homogeneous response to radiation 7
Conventional NTCP models The most well-known and traditionally accepted DVH-based methods: Lyman-Kutcher-Burman (LKB) Relative Seriality (RS) 8
LKB model Lyman, Rad Res 1985, S13-S19 Burman, IJROBP 1991, 21: 123-135 9
LKB model & geud Nimierko, Med Phys 1997, 1: 103 110 10
RS model Kallman, Phys Med Biol 1992, 37: 871-90 11
Radiotherapy DATA & NTCP 3 categories of RO data (besides dose): clinical data Onjukka, Med Phys,2015, 42:236-41; Cella, IJROBP 2013, 87:304-310 biological markers Bentzen, IJROBP 2010, 76: S145-50 imaging features Jerai, IJROBP 2010, 76: S145-50 12
Multi-variable modelling approach The observed RO outcome is considered as the result of functional mapping of several input variables Tommasino, TCR 2017, 6 (S5): S807-S821 13
logistic regression Conventional statistical inference 14
MV logistic regression Data-driven approach 15
1.Pre-processing: cross-correlation matrix Modelling steps 2. MV Model order estimation 3. Most frequently selected models by bootstrap sampling technique Cella, IJROBP 2013, 87: 304-310 4. MV Model predictive power Rs = 0.57, P<.001 16
NTCP & clinical features A large number of studies have explored the benefits of incorporating clinical factors into NTCP modelling, for example lung toxicity Huang, Acta Oncol 2011, 50:51-60; Cella, Radiother Oncol 2015, 117_36-43 rectal toxicity Defraene, IJROBP 2012, 82:1233-42, Rancati, Radiother Oncol 2011, 100:124-30, Cella, Radiat Oncol, 201, 8:221 17
Radiation pneumonitis Optimal NTCP : Heart D10, lung D35 and lung maximum dose Huang, Acta Oncol 2011, 50:51-60 GI toxicity Optimal NTCP : Rectum V65, AH/AC drugs and acute GI tox Cella, Radiat Oncol, 2015, 8:221 18
NTCP& Biomarkers The inclusion of biological variability could improve predictive capability and explain individual susceptibility to radiation. Most investigated biomarkers in RT outcome models: plasma biomarkers (e.g. inflammatory cytokines) Fu, IJROBP 2001, 50: 899-908, Stenmark, IJROBP 2012, 84: e217-22 genetic variables (e.g. single nucleotide polymorphism, SNPs) Coates, Radiother Oncol 2015, 115:107-13, Tucker, IJROBP 2013; 85:107-13 19
Lung toxicity Rectal toxicity Combining IL-8, TGF-ß1 and MLD improved the predictive ability compared to either variable alone (AUC=0,8) Stenmark, IJROBP 2012, 84: e217-22 Integration of SNP and CNV improved prediction of Rectal Bleeding and Erectile Dysfunction Coates, Radiother Oncol 2015, 115:107-13 20
NTCP & Imaging Technological advances in biomedical imaging have resulted in the increasing popularity of quantitative clinical imaging studies Global measures (e.g. mean CT number or PET Standardized Uptake Value ) Texture features (i.e. mathematical parameters able to describe the grey-level patterns of an image) 21
Higher baseline lung density prognostic for higher DeltaHUmax in SABR patients Thoracic CT Relationship between dose, DFV and RP development Defraene, Radiother Oncol. 2015,117: 29-35 Cunliffe, IJROBP. 2015,91: 11048-56 22
High input dimensionality One of the biggest challenges in multivariate modeling m variables>> n patents Possible improvement : LR with penalty techniques, e.g. Least Absolute Shrinkage and Selection Operator (LASSO) 23
LR is typically limited to linearly separable data v 2 What if we are interested in more complex nonlinearly separable data? v 1 v 2 Kang, IJROBP 2015, 93: e1127-1135 v 1 24
Machine learning Class of computational algorithms which are able to learn from the surrounding environments and to detect nonlinear complex patterns in given input data. Popular ML approaches in RO: 1. Artificial Neural Networks 2. Support Vector Machine 3. Bayesian Networks https://xkcd.com/1838/
Beyond DVH DVH approach disregards the potential influence of spatial distribution of the dose in radiation induced morbidity On courtesy of G Palma 26
Voxel based analysis (VBA) aims to identify correlations between radiation-induced morbidity and local dose release VBA permits to: Voxel-based approach Overcome the limitations of DVH-based or single organ analysis; Avoid the cumbersome contouring of very many, elusive OARs.
Investigated endpoints Rectal toxicity in prostate cancer pts Acosta, Phys. Med. Biol. 2013, 58: 2581 2595 Lung toxicity in Hodgkin lymphoma pts Palma, IJROBP 2016, 96: e127-133 Dysphagia in Head & Neck pts Monti, Sci Rep 2017, 7: 7220 Survival in lung cancer pts McWilliam, Eur J Cancer 2017, 85: e106-1132017
our VBA pipeline 1. Inter-patient Elastic Image Registration (EIR) of the planning CTs on a Common Coordinate System (CCS); 2. Mapping of the dose of each patient into the CCS by the obtained deformation fields; 3. Comparison of dose map distributions associated to patients who developed toxicity and who did not by a non-parametric multiple permutation testing with threshold-free cluster enhancement [TFCE-test] for inference on imaging data; 4. Generation of the corresponding p < 0.05 voxel clusters (S0.05). Palma, IJROBP 2016, 96: e127-133
Flow chart Planning CTs Dose Maps VBA Deformation Fields Warped Dose Maps Mov Fix EIR Dose Warp TFCE S 0.05 CCS Monti, Sci Rep 2017, 7: 7220
Rectal bleeding (a) Mean differences in dose between the two groups. (b) 3Dconstruction of lateral views of the common template, highlighting the rectal region R1, where these differences were statistically significant (p < 0.01). Acosta, Phys. Med. Biol. 2013, 58: 2581 2595
LUNg damage Palma, IJROBP 2016, 96: e127-133 Coronal view of the mean dose map for patients (a) who experienced lung damage and (b) for patients who did not; (c) corresponding dose difference maps Coronal view of lung subregions showing a statistically significant dose difference between groups (P<.05) according to (d) permutation test T, (e) TFCE test, and (f) voxel-wise 2-sample t test. The color map represents Log p.
Dysphagia Dose distributions given to patients who developed dysphagia (a) and who did not (b) were compared; The VBA showed that a significantly higher dose was delivered to disphagia patients (c) in two voxel clusters (d) located in correspondence of the cricopharyngeus muscle and the cervical esophagus. Monti, Sci Rep 2017, 7: 7220
SURVIVAL VBA identified that the base of the heart is a dosesensitive region, strongly correlated with lung cancer patient survival. Axial, sagittal and coronal views from the computed tomography scan of the reference patient with statistical significance (p < 0.001) McWilliam, Eur J Cancer 2017, 85: e106-1132017
Take home message The potential advantages that could arise from the enhanced NTCP models are evident, but there is a need for further research involving both mathematical and technical study, as well as proof-of-concept pre-clinical and (ideally) clinical studies!! 35
Thank you!!! 36