Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging

Abstract

This article describes a macroscopic mathematical modeling approach to capture the interplay between solid tumor evolution and cell damage during radiotherapy. Volume regression profiles of 15 patients with uterine cervical cancer were reconstructed from serial cone-beam computed tomography data sets, acquired for image-guided radiotherapy, and used for model parameter learning by means of a genetic-based optimization. Patients, diagnosed with either squamous cell carcinoma or adenocarcinoma, underwent different treatment modalities (image-guided radiotherapy and image-guided chemo-radiotherapy). The mean volume at the beginning of radiotherapy and the end of radiotherapy was on average 23.7 cm 3 (range: 12.7-44.4 cm 3 ) and 8.6 cm 3 (range: 3.6-17.1 cm 3 ), respectively. Two different tumor dynamics were taken into account in the model: the viable (active) and the necrotic cancer cells. However, according to the results of a preliminary volume regression analysis, we assumed a short dead cell resolving time and the model was simplified to the active tumor volume. Model learning was performed both on the complete patient cohort (cohort-based model learning) and on each single patient (patient-specific model learning). The fitting results (mean error: *16% and *6% for the cohort-based model and patient-specific model, respectively) highlighted the model ability to quantitatively reproduce tumor regression. Volume prediction errors of about 18% on average were obtained using cohort-based model computed on all but 1 patient at a time (leave-one-out technique). Finally, a sensitivity analysis was performed and the data uncertainty effects evaluated by simulating an average volume perturbation of about 1.5 cm 3 obtaining an error increase within 0.2%. In conclusion, we showed that simple time-continuous models can represent tumor regression curves both on a patient cohort and patient-specific basis; this discloses the opportunity in the future to exploit such models to predict how changes in the treatment schedule (number of fractions, doses, intervals among fractions) might affect the tumor regression on an individual basis.Keywords tumor mathematical modeling, uterine cervical cancer, radiosensitivity, linear-quadratic model, image-guided radiotherapy Abbreviations ADC, adenocarcinoma; CBCT, cone-beam computed tomography; ChT, chemotherapy; cM, cohort-based model; cMg, cohortbased model learning using Gompertzian growth function; cMl, cohort-based model learning using logistic growth function; CT, computed tomography; GTV, gross tumor volume; PTV, planning target volume; IGRT, image-guided radiotherapy; K, tumor cell carrying capacity of the tissue; LOO, leave-one-out; LQ, linear-quadratic; MRI, magnetic resonance imaging; ODE, ordinary differential equation; PET, positron emission tomography; pM, patient-specific model; pMg, patient-specific model learning using Gompertzian growth function; pMg5, pMg performed on the reduced data set of patient 5; pMg5*, pMg performed on the complete data set of patient 5;...

IMRT and brachytherapy comparison in gynaecological cancer treatment: thinking over dosimetry and radiobiology

“…First, regarding the potential increasing risk of second malignancies due to the low-dose irradiation of normal tissues [49]. Second, IMRT planning needs to take into consideration both organ motion and tumour regression, as several studies have reported on the inter-fraction and intra-fraction motions of the cervix [50][51][52][53][54][55]. In fact, Haripotepornkul et al [56] in their study concluded that daily image guidance and possibly re-planning of the treatment volumes are necessary to improve the accuracy of IMRT by accounting for the unpredictable changes in cervical position.…”

Tumor radio‐sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats

“…It reflects the cell-line-specific growth rate and, possibly, environmental factors influencing the cell-cycle and tumor aggressiveness. The radiation therapy effects are usually modeled by means of the linear-quadratic (LQ) model, instead 23,31 . It defines the surviving fraction $\left(\text{SF}\right)$ as $true\text{SF}=e^{-\alpha d\left(1-\frac{d}{\alpha /\beta }\right)},$ where d is the delivered dose and the tumor radio-sensitivity is represented by the α (Gy −1 ) and β (Gy −2 ) parameters accounting for double (lethal) and single (possibly reparable) strand break damage to DNA, respectively.…”

“…(1)–(3b)]. Similarly to our previous work, 23,26 the free parameters ( T d , α , and T 1/2 ) were optimized on an animal-specific basis by means of a custom genetic algorithm in order to achieve the best total volume fitting (∀ t , $V\left(t\right)\sim V_v\left(t\right)+V_n\left(t\right)$). In the parameter learning, T d , T 1/2 , and α were bounded in the range 3–7 days, 1–60 days, and 0.005–0.5 Gy −1 , respectively, according to the prior literature 22,33 .…”

Radiosensitivity in HeLa cervical cancer cells overexpressing glutathione S-transferase π 1

“…Recent studies have demonstrated that 70% of cancer patients require adjuvant radiotherapy during their treatment course (3,4). For specific tumor types, including nasopharyngeal, esophageal and prostate cancer tumors, radiotherapy may even replace surgical treatment (3).…”

“…However, for specific cases of advanced or metastasized tumors with low radiosensitivity, the increased dose of radiation required may damage the surrounding healthy tissues and organs (3). Therefore, the identification of novel methods to enhance tumor cell radiosensitivity has become a recent focus of medical radiation research (4).…”