Neutron Autoradiography Combined With UV-C Sensitization: Toward the Intracellular Localization of Boron.

Our group has reported the imprint formation of biological material on polycarbonate nuclear track detectors by UV-C exposure, which is used as an approach to simultaneously visualize cell imprints and nuclear tracks coming from the boron neutron capture reaction. Considering that the cell nucleus has a higher UV-C absorption than the cytoplasm and that hematoxylin preferentially stains the nucleus, we proposed to enhance the contrast between these two main cell structures by hematoxylin staining before UV-C sensitization. In this study, several experiments were performed in order to optimize UV-C exposure parameters and chemical etching conditions for cell imprint formation using the SK-BR-3 breast cancer cell line. The proposed method improves significantly the resolution of the cell imprints. It allows clear differentiation of the nucleus from the rest of the cell, together with nuclear tracks pits. Moreover, it reduces considerably the UV-C exposure time, an important experimental issue. The proposed methodology can be applied to study the boron distribution independently from the chosen cell line and/or boron compounds.

Assessing advantages of sequential boron neutron capture therapy (BNCT) in an oral cancer model with normalized blood vessels.

BACKGROUND: We previously demonstrated the therapeutic success of sequential boron neutron capture therapy (Seq-BNCT) in the hamster cheek pouch oral cancer model. It consists of BPA-BNCT followed by GB-10-BNCT 24 or 48 hours later. Additionally, we proved that tumor blood vessel normalization with thalidomide prior to BPA-BNCT improves tumor control. The aim of the present study was to evaluate the therapeutic efficacy and explore potential boron microdistribution changes in Seq-BNCT preceded by tumor blood vessel normalization. MATERIAL AND METHODS: Tumor bearing animals were treated with thalidomide for tumor blood vessel normalization, followed by Seq-BNCT (Th+ Seq-BNCT) or Seq-Beam Only (Th+ Seq-BO) in the window of normalization. Boron microdistribution was assessed by neutron autoradiography. RESULTS: Th+ Seq-BNCT induced overall tumor response of 100%, with 87 (4)% complete tumor response. No cases of severe mucositis in dose-limiting precancerous tissue were observed. Differences in boron homogeneity between tumors pre-treated and not pre-treated with thalidomide were observed. CONCLUSION: Th+ Seq-BNCT achieved, for the first time, response in all treated tumors. Increased homogeneity in tumor boron microdistribution is associated to an improvement in tumor control.

From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy.

Knowledge of the 10B microdistribution is of great relevance in BNCT studies. Since 10B concentration assesment through neutron autoradiography depends on the correct quantification of tracks in a nuclear track detector, image acquisition and processing conditions should be controlled and verified, in order to obtain accurate results to be applied in the frame of BNCT. With this aim, an image verification process was proposed, based on parameters extracted from the quantified nuclear tracks. Track characterization was performed by selecting a set of morphological and pixel-intensity uniformity parameters from the quantified objects (area, diameter, roundness, aspect ratio, heterogeneity and clumpiness). Their distributions were studied, leading to the observation of varying behaviours in images generated by different samples and acquisition conditions. The distributions corresponding to samples coming from the BNC reaction showed similar attributes in each analyzed parameter, proving to be robust to the experimental process, but sensitive to light and focus conditions. Considering those observations, a manual feature extraction was performed as a pre-processing step. A Support Vector Machine (SVM) and a fully dense Neural Network (NN) were optimized, trained, and tested. The final performance metrics were similar for both models: 93%-93% for the SVM, vs 94%-95% for the NN in accuracy and precision respectively. Based on the distribution of the predicted class probabilities, the latter had a better capacity to reject inadequate images, so the NN was selected to perform the image verification step prior to quantification. The trained NN was able to correctly classify the images regardless of their track density. The exhaustive characterization of the nuclear tracks provided new knowledge related to the autoradiographic images generation. The inclusion of machine learning in the analysis workflow proves to optimize the boron determination process and paves the way for further applications in the field of boron imaging.