Combining the LKB NTCP model with radiosensitivity parameters to characterize toxicity of radionuclides based on a multiclonogen kidney model: a theoretical assessment

Heterogeneity of absorbed dose distribution in kidney tissues and dose-response modelling of nephrotoxicity in radiopharmaceutical therapy with beta-particle emitters: A review

Absorbed dose heterogeneity in kidney tissues is an important issue in radiopharmaceutical therapy. The effect of absorbed dose heterogeneity in nephrotoxicity is, however, not fully understood yet, which hampers the implementation of treatment optimization by obscuring the interpretation of clinical response data and the selection of optimal treatment options. Although some dosimetry methods have been developed for kidney dosimetry to the level of microscopic renal substructures, the clinical assessment of the microscopic distribution of radiopharmaceuticals in kidney tissues currently remains a challenge. This restricts the anatomical resolution of clinical dosimetry, which hinders a thorough clinical investigation of the impact of absorbed dose heterogeneity. The potential of absorbed dose-response modelling to support individual treatment optimization in radiopharmaceutical therapy is recognized and gaining attraction. However, biophysical modelling is currently underexplored for the kidney, where particular modelling challenges arise from the convolution of a complex functional organization of renal tissues with the function-mediated dose distribution of radiopharmaceuticals. This article reviews and discusses the heterogeneity of absorbed dose distribution in kidney tissues and the absorbed dose-response modelling of nephrotoxicity in radiopharmaceutical therapy. The review focuses mainly on the peptide receptor radionuclide therapy with beta-particle emitting somatostatin analogues, for which the scientific literature reflects over two decades of clinical experience. Additionally, detailed research perspectives are proposed to address various identified challenges to progress in this field.

Dosimetric predictors and Lyman normal tissue complication probability model of hematological toxicity in cervical cancer patients with treated with pelvic irradiation

PURPOSE

To identify dosimetric parameters associated with acute hematological toxicity (HT) and identify the corresponding normal tissue complication probability (NTCP) model in cervical cancer patients receiving helical tomotherapy (Tomo) or fixed-field intensity-modulated radiation therapy (ff-IMRT) in combination with chemotherapy, that is, concurrent chemoradiotherapy (CCRT) using the Lyman-Kutcher-Burman normal tissue complication probability (LKB-NTCP) model.

METHODS

Data were collected from 232 cervical cancer patients who received Tomo or ff-IMRT from 2015 to 2018. The pelvic bone marrow (PBM) (including the ilium, pubes, ischia, acetabula, proximal femora, and lumbosacral spine) was contoured from the superior boundary (usually the lumbar 5 vertebra) of the planning target volume (PTV) to the proximal end of the femoral head (the lower edge of the ischial tubercle). The parameters of the LKB model predicting ≥grade 2 hematological toxicity (Radiation Therapy Oncology Group [RTOG] grading criteria) (TD50 (1), m, and n) were determined using maximum likelihood analyses. Univariate and multivariate logistic regression analyses were used to identify correlations between dose-volume parameters and the clinical factors of HT.

RESULTS

In total, 212 (91.37%) patients experienced ≥grade 2 hematological toxicity. The fitted normal tissue complication probability model parameters were TD50 (1) = 38.90 Gy (95%CI, [36.94, 40.96]), m = 0.13 (95%CI [0.12, 0.16]), and n = 0.04 (95%CI [0.02, 0.05]). Per the univariate analysis, the NTCP (the use of LKB-NTCP with the set of model parameters found, p = 0.023), maximal PBM dose (p = 0.01), mean PBM dose (p = 0.021), radiation dose (p = 0.001), and V16-53 (p < 0. 05) were associated with ≥grade 2 HT. The NTCP (the use of LKB-NTCP with the set of model parameters found, p = 0.023; AUC = 0.87), V16, V17, and V18 ≥ 79.65%, 75.68%, and 72.65%, respectively (p < 0.01, AUC = 0.66∼0.68), V35 and V36 ≥ 30.35% and 28.56%, respectively (p < 0.05; AUC = 0.71), and V47 ≥ 13.43% (p = 0.045; AUC = 0.80) were significant predictors of ≥grade 2 hematological toxicity from the multivariate logistic regression analysis.

CONCLUSIONS

The volume of the PBM of patients treated with concurrent chemoradiotherapy and subjected to both low-dose (V16-18 ) and high-dose (V35,36 and V47 ) irradiation was associated with hematological toxicity, depending on the fractional volumes receiving the variable degree of dosage. The NTCP were stronger predictors of toxicity than V16-18 , V35, 36 , and V47 . Hence, avoiding radiation hot spots on the PBM could reduce the incidence of severe HT.

Exposure-response modeling improves selection of radiation and radiosensitizer combinations

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.