Normal tissue complication probability modeling of radiation-induced sensorineural hearing loss after head-and-neck radiation therapy

A machine learning approach for prediction of auditory brain stem response in patients after head-and-neck radiation therapy

Objective

The present study aimed to assess machine learning (ML) models according to radiomic features to predict ototoxicity using auditory brain stem responses (ABRs) in patients with radiation therapy (RT) for head-and-neck cancers.

Materials and Methods

The ABR test was performed on 50 patients having head-and-neck RT. Radiomic features were extracted from the brain stem in computed tomography images to generate a radiomic signature. Moreover, accuracy, sensitivity, specificity, the area under the curve, and mean cross-validation were used to evaluate six different ML models.

Results

Out of 50 patients, 21 participants experienced ototoxicity. Furthermore, 140 radiomic features were extracted from the segmented area. Among the six ML models, the Random Forest method with 77% accuracy provided the best result.

Conclusion

According to the ML approach, we showed the relatively high prediction power of the radiomic features in radiation-induced ototoxicity. To better predict the outcomes, future studies on a larger number of participants are recommended.

Ototoxicity prognostic models in adult and pediatric cancer patients: a rapid review

PURPOSE

A cornerstone of treatment for many cancers is the administration of platinum-based chemotherapies and/or ionizing radiation, which can be ototoxic. An accurate ototoxicity risk assessment would be useful for counseling, treatment planning, and survivorship follow-up in patients with cancer.

METHODS

This systematic review evaluated the literature on predictive models for estimating a patient's risk for chemotherapy-related auditory injury to accelerate development of computational approaches for the clinical management of ototoxicity in cancer patients. Of the 1195 articles identified in a PubMed search from 2010 forward, 15 studies met inclusion for the review.

CONCLUSIONS

All but 1 study used an abstraction of the audiogram as a modeled outcome; however, specific outcome measures varied. Consistently used predictors were age, baseline hearing, cumulative cisplatin dose, and radiation dose to the cochlea. Just 5 studies were judged to have an overall low risk of bias. Future studies should attempt to minimize bias by following statistical best practices including not selecting multivariate predictors based on univariate analysis, validation in independent cohorts, and clearly reporting the management of missing and censored data. Future modeling efforts should adopt a transdisciplinary approach to define a unified set of clinical, treatment, and/or genetic risk factors. Creating a flexible model that uses a common set of predictors to forecast the full post-treatment audiogram may accelerate work in this area. Such a model could be adapted for use in counseling, treatment planning, and follow-up by audiologists and oncologists and could be incorporated into ototoxicity genetic association studies as well as clinical trials investigating otoprotective agents.

IMPLICATIONS FOR CANCER SURVIVORS

Improvements in the ability to model post-treatment hearing loss can help to improve patient quality of life following cancer care. The improvements advocated for in this review should allow for the acceleration of advancements in modeling the auditory impact of these treatments to support treatment planning and patient counseling during and after care.

Prediction of chronic kidney disease in abdominal cancers radiation therapy using the functional assays of normal tissue complication probability models

Aim

The purpose of this study is to predict chronic kidney disease (CKD) in the radiotherapy of abdominal cancers by evaluating clinical and functional assays of normal tissue complication probability (NTCP) models.

Materials and Methods

Radiation renal damage was analyzed in 50 patients with abdominal cancers 12 months after radiotherapy through a clinical estimated glomerular filtration rate (eGFR). According to the common terminology criteria for the scoring system of adverse events, Grade 2 CKD (eGFR ≤30-59 ml/min/1.73 m²) was considered as the radiation therapy endpoint. Modeling and parameter estimation of NTCP models were performed for the Lyman-equivalent uniform dose (EUD), the logit-EUD critical volume (CV), the relative seriality, and the mean dose model.

Results

The confidence interval of the fitted parameters was 95%. The parameter value of D₅₀ was obtained 22-38 Gy, and the n and s parameters were equivalent to 0.006 -3 and 1, respectively. According to the Akaike's information criterion, the mean dose model predicts radiation-induced CKD more accurately than the other models.

Conclusion

Although the renal medulla consists of many nephrons arranged in parallel, each nephron has a seriality architecture as renal functional subunits. Therefore, based on this principle and modeling results in this study, the whole kidney organs may have a serial-parallel combination or a secret architecture.

Normal tissue complication probability modeling to guide individual treatment planning in pediatric cranial proton and photon radiotherapy

PURPOSE

Proton therapy (PT) is broadly accepted as the gold standard of care for pediatric patients with cranial cancer. The superior dose distribution of PT compared to photon radiotherapy reduces normal tissue complication probability (NTCP) for organs at risk. As NTCPs for pediatric organs are not well understood, clinics generally base radiation response on adult data. However, there is evidence that radiation response strongly depends on the age and even sex of a patient. Furthermore, questions surround the influence of individual intrinsic radiosensitivity (α/β ratio) on pediatric NTCP. While the clinical pediatric NTCP data is scarce, radiobiological modeling and sensitivity analyses can be used to investigate the NTCP trends and its dependence on individual modeling parameters. The purpose of this study was to perform sensitivity analyses of NTCP models to ascertain the dependence of radiosensitivity, sex, and age of a child and predict cranial side-effects following intensity-modulated proton therapy (IMPT) and intensity-modulated radiotherapy (IMRT).

METHODS

Previously, six sex-matched pediatric cranial datasets (5, 9, and 13 years old) were planned in Varian Eclipse treatment planning system (13.7). Up to 108 scanning beam IMPT plans and 108 IMRT plans were retrospectively optimized for a range of simulated target volumes and locations. In this work, dose-volume histograms were extracted and imported into BioSuite Software for radiobiological modeling. Relative-Seriality and Lyman-Kutcher-Burman models were used to calculate NTCP values for toxicity endpoints, where TD50, (based on reported adult clinical data) was varied to simulate sex dependence of NTCP. Plausible parameter ranges, based on published literature for adults, were used in modeling. In addition to sensitivity analyses, a 20% difference in TD50 was used to represent the radiosensitivity between the sexes (with females considered more radiosensitive) for ease of data comparison as a function of parameters such as α/β ratio.

RESULTS

IMPT plans resulted in lower NTCP compared to IMRT across all models (p < 0.0001). For medulloblastoma treatment, the risk of brainstem necrosis (> 10%) and cochlea tinnitus (> 20%) among females could potentially be underestimated considering a lower TD50 value for females. Sensitivity analyses show that the difference in NTCP between sexes was significant (p < 0.0001). Similarly, both brainstem necrosis and cochlea tinnitus NTCP varied significantly (p < 0.0001) across tested α/β as a function of TD50 values (assumption being that TD50 values are 20% lower in females). If the true α/β of these pediatric tissues is higher than expected (α/β ∼ 3), the risk of tinnitus for IMRT can significantly increase (p < 0.0001).

CONCLUSION

Due to the scarcity of pediatric NTCP data available, sensitivity analyses were performed using plausible ranges based on published adult data. In the clinical scenario where, if female pediatric patients were 20% more radiosensitive (lower TD50 value), they could be up to twice as likely to experience side-effects of brainstem necrosis and cochlea tinnitus compared to males, highlighting the need for considering the sex in NTCP models. Based on our sensitivity analyses, age and sex of a pediatric patient could significantly affect the resultant NTCP from cranial radiotherapy, especially at higher α/β values.

Effects of Adipose-Derived Stem Cells and Platelet-Rich Plasma for Prevention of Alopecia and Other Skin Complications of Radiotherapy

BACKGROUND

Radiotherapy (RT) involves the use of ionizing radiation in treating malignancies and benign disorders. However, RT damages target and healthy surrounding tissues in a dose-dependent manner. This effectively reduces patient compliance and quality of life, thereby warranting the prevention of RT-induced adverse effects on skin. Adipose-derived stem cells (ASCs) are used to treat RT-induced damage and platelet-rich plasma (PRP) provides a scaffold that potentiates the effects of ASCs. Thus, the aim of this study was to determine the mechanism employed by ASCs and PRP in protecting against RT-induced adverse effects.

METHODS

We have established an immunodeficient mouse transplantation model using which human hair follicular units were implanted. When the follicular units were macroscopically and microscopically mature and anagenic, we administered localized RT. Subsequently, the mice were randomly divided into 4 groups based on the subcutaneous injection of the following to the irradiated transplantation site: saline, PRP, ASCs, and a combination of ASCs and PRP. Next, we used macroscopic and microscopic analyses to determine the protective effects of the injected solutions on skin and hair follicles.

RESULTS

Adipose-derived stem cells reduced RT-induced adverse effects, such as impaired wound healing, alopecia, skin atrophy, and fibrosis by suppressing inflammation, dystrophy, degeneration, connective tissue synthesis, and apoptosis and increasing cellular proliferation, differentiation, and signaling. Moreover, these effects were augmented by PRP.

CONCLUSIONS

Thus, co-administering ASCs with PRP in mice prevented RT-induced adverse effects and can be tested for use in clinical practice.

Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models

INTRODUCTION

We aimed to assess the power of radiomic features based on computed tomography to predict risk of chronic kidney disease in patients undergoing radiation therapy of abdominal cancers.

METHODS

50 patients were evaluated for chronic kidney disease 12 months after completion of abdominal radiation therapy. At the first step, the region of interest was automatically extracted using deep learning models in computed tomography images. Afterward, a combination of radiomic and clinical features was extracted from the region of interest to build a radiomic signature. Finally, six popular classifiers, including Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, Random Forest, and Support Vector Machine, were used to predict chronic kidney disease. Evaluation criteria were as follows: accuracy, sensitivity, specificity, and area under the ROC curve.

RESULTS

Most of the patients (58%) experienced chronic kidney disease. A total of 140 radiomic features were extracted from the segmented area. Among the six classifiers, Random Forest performed best with the accuracy and AUC of 94% and 0.99, respectively.

CONCLUSION

Based on the quantitative results, we showed that a combination of radiomic and clinical features could predict chronic kidney radiation toxicities. The effect of factors such as renal radiation dose, irradiated renal volume, and urine volume 24-h on CKD was proved in this study.

Cochlea sparing optimized radiotherapy for nasopharyngeal carcinoma

BACKGROUND

Definitive chemoradiotherapy (CRT) is standard of care for nasopharyngeal carcinoma. Due to the tumor localization and concomitant platinum-based chemotherapy, hearing impairment is a frequent complication, without defined dose-threshold. In this study, we aimed to achieve the maximum possible cochleae sparing.

MATERIALS AND METHODS

Treatment plans of 20 patients, treated with CRT (6 IMRT and 14 VMAT) based on the QUANTEC organs-at-risk constraints were investigated. The cochleae were re-delineated independently by two radiation oncologists, whereas target volumes and other organs at risk (OARs) were not changed. The initial plans, aiming to a mean cochlea dose < 45 Gy, were re-optimized with VMAT, using 2-2.5 arcs without compromising the dose coverage of the target volume. Mean cochlea dose, PTV coverage, Homogeneity Index, Conformity Index and dose to other OAR were compared to the reference plans. Wilcoxon signed-rank test was used to evaluate differences, a p value < 0.05 was considered significant.

RESULTS

The re-optimized plans achieved a statistically significant lower dose for both cochleae (median dose for left and right 14.97 Gy and 18.47 Gy vs. 24.09 Gy and 26.05 Gy respectively, p < 0.001) compared to the reference plans, without compromising other plan quality parameters. The median NTCP for tinnitus of the most exposed sites was 11.3% (range 3.52-91.1%) for the original plans, compared to 4.60% (range 1.46-90.1%) for the re-optimized plans (p < 0.001). For hearing loss, the median NTCP of the most exposed sites could be improved from 0.03% (range 0-99.0%) to 0.00% (range 0-98.5%, p < 0.001).

CONCLUSIONS

A significantly improved cochlea sparing beyond current QUANTEC constraints is feasible without compromising the PTV dose coverage in nasopharyngeal carcinoma patients treated with VMAT. As there appears to be no threshold for hearing toxicity after CRT, this should be considered for future treatment planning.