Iterative image reconstruction algorithm analysis for optical CT radiochromic gel dosimetry.

Background.Modern radiation therapy technologies aim to enhance radiation dose precision to the tumor and utilize hypofractionated treatment regimens. Verifying the dose distributions associated with these advanced radiation therapy treatments remains an active research area due to the complexity of delivery systems and the lack of suitable three-dimensional dosimetry tools. Gel dosimeters are a potential tool for measuring these complex dose distributions. A prototype tabletop solid-tank fan-beam optical CT scanner for readout of gel dosimeters was recently developed. This scanner does not have a straight raypath from source to detector, thus images cannot be reconstructed using filtered backprojection (FBP) and iterative techniques are required.Purpose.To compare a subset of the top performing algorithms in terms of image quality and quantitatively determine the optimal algorithm while accounting for refraction within the optical CT system. The following algorithms were compared: Landweber, superiorized Landweber with the fast gradient projection perturbation routine (S-LAND-FGP), the fast iterative shrinkage/thresholding algorithm with total variation penalty term (FISTA-TV), a monotone version of FISTA-TV (MFISTA-TV), superiorized conjugate gradient with the nonascending perturbation routine (S-CG-NA), superiorized conjugate gradient with the fast gradient projection perturbation routine (S-CG-FGP), superiorized conjugate gradient with with two iterations of CG performed on the current iterate and the nonascending perturbation routine (S-CG-2-NA).Methods.A ray tracing simulator was developed to track the path of light rays as they traverse the different mediums of the optical CT scanner. Two clinical phantoms and several synthetic phantoms were produced and used to evaluate the reconstruction techniques under known conditions. Reconstructed images were analyzed in terms of spatial resolution, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal non-uniformity (SNU), mean relative difference (MRD) and reconstruction time. We developed an image quality based method to find the optimal stopping iteration window for each algorithm. Imaging data from the prototype optical CT scanner was reconstructed and analysed to determine the optimal algorithm for this application.Results.The optimal algorithms found through the quantitative scoring metric were FISTA-TV and S-CG-2-NA. MFISTA-TV was found to behave almost identically to FISTA-TV however MFISTA-TV was unable to resolve some of the synthetic phantoms. S-CG-NA showed extreme fluctuations in the SNR and CNR values. S-CG-FGP had large fluctuations in the SNR and CNR values and the algorithm has less noise reduction than FISTA-TV and worse spatial resolution than S-CG-2-NA. S-LAND-FGP had many of the same characteristics as FISTA-TV; high noise reduction and stability from over iterating. However, S-LAND-FGP has worse SNR, CNR and SNU values as well as longer reconstruction time. S-CG-2-NA has superior spatial resolution to all algorithms while still maintaining good noise reduction and is uniquely stable from over iterating.Conclusions.Both optimal algorithms (FISTA-TV and S-CG-2-NA) are stable from over iterating and have excellent edge detection with ESF MTF 50% values of 1.266 mm-1and 0.992 mm-1. FISTA-TV had the greatest noise reduction with SNR, CNR and SNU values of 424, 434 and 0.91 × 10-4, respectively. However, low spatial resolution makes FISTA-TV only viable for large field dosimetry. S-CG-2-NA has better spatial resolution than FISTA-TV with PSF and LSF MTF 50% values of 1.581 mm-1and 0.738 mm-1, but less noise reduction. S-CG-2-NA still maintains good SNR, CNR, and SNU values of 168, 158 and 1.13 × 10-4, respectively. Thus, S-CG-2-NA is a well rounded reconstruction algorithm that would be the preferable choice for small field dosimetry.

Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network.

Reprogramming of cellular metabolism is a driving factor of tumour progression and radiation therapy resistance. Identifying biochemical signatures associated with tumour radioresistance may assist with the development of targeted treatment strategies to improve clinical outcomes. Raman spectroscopy (RS) can monitor post-irradiation biomolecular changes and signatures of radiation response in tumour cells in a label-free manner. Convolutional Neural Networks (CNN) perform feature extraction directly from data in an end-to-end learning manner, with high classification performance. Furthermore, recently developed CNN explainability techniques help visualize the critical discriminative features captured by the model. In this work, a CNN is developed to characterize tumour response to radiotherapy based on its degree of radioresistance. The model was trained to classify Raman spectra of three human tumour cell lines as radiosensitive (LNCaP) or radioresistant (MCF7, H460) over a range of treatment doses and data collection time points. Additionally, a method based on Gradient-Weighted Class Activation Mapping (Grad-CAM) was used to determine response-specific salient Raman peaks influencing the CNN predictions. The CNN effectively classified the cell spectra, with accuracy, sensitivity, specificity, and F1 score exceeding 99.8%. Grad-CAM heatmaps of H460 and MCF7 cell spectra (radioresistant) exhibited high contributions from Raman bands tentatively assigned to glycogen, amino acids, and nucleic acids. Conversely, heatmaps of LNCaP cells (radiosensitive) revealed activations at lipid and phospholipid bands. Finally, Grad-CAM variable importance scores were derived for glycogen, asparagine, and phosphatidylcholine, and we show that their trends over cell line, dose, and acquisition time agreed with previously established models. Thus, the CNN can accurately detect biomolecular differences in the Raman spectra of tumour cells of varying radiosensitivity without requiring manual feature extraction. Finally, Grad-CAM may help identify metabolic signatures associated with the observed categories, offering the potential for automated clinical tumour radiation response characterization.

Understanding radiation response and cell cycle variation in brain tumour cells using Raman spectroscopy.

Radiation therapy is currently utilised in the treatment of approximately 50% of cancer patients. A move towards patient tailored radiation therapy would help to improve the treatment outcome for patients as the inter-patient and intra-patient heterogeneity of cancer leads to large differences in treatment responses. In radiation therapy, a typical treatment outcome is cell cycle arrest which leads to cell cycle synchronisation. As treatment is typically given over multiple fractions it is important to understand how variation in the cell cycle can affect treatment response. Raman spectroscopy has previously been assessed as a method for monitoring radiation response in cancer cells and has shown promise in detecting the subtle biochemical changes following radiation exposure. This study evaluated Raman spectroscopy as a potential tool for monitoring cellular response to radiation in synchronised versus unsynchronised UVW human glioma cells in vitro. Specifically, it was hypothesised that the UVW cells would demonstrate a greater radiation resistance if the cell cycle phase of the cells was synchronised to the G1/S boundary prior to radiation exposure. Here we evaluated whether Raman spectroscopy, combined with cell cycle analysis and DNA damage and repair analysis (γ-H2AX assay), could discriminate the subtle cellular changes associated with radiation response. Raman spectroscopy combined with principal component analysis (PCA) was able to show the changes in radiation response over 24 hours following radiation exposure. Spectral changes were assigned to variations in protein, specifically changes in protein signals from amides as well as changes in lipid expression. A different response was observed between cells synchronised in the cell cycle and unsynchronised cells. After 24 hours following irradiation, the unsynchronised cells showed greater spectral changes compared to the synchronised cells demonstrating that the cell cycle plays an important role in the radiation resistance or sensitivity of the UVW cells, and that radiation resistance could be induced by controlling the cell cycle. One of the main aims of cancer treatment is to stop the proliferation of cells by controlling or halting progression through the cell cycle, thereby highlighting the importance of controlling the cell cycle when studying the effects of cancer treatments such as radiation therapy. Raman spectroscopy has been shown to be a useful tool for evaluating the changes in radiation response when the cell cycle phase is controlled and therefore highlighting its potential for assessing radiation response and resistance.

Radiomic analysis for early differentiation of lung cancer recurrence from fibrosis in patients treated with lung stereotactic ablative radiotherapy.

Objective. The development of radiation-induced fibrosis after stereotactic ablative radiotherapy (SABR) can obscure follow-up images and delay detection of a local recurrence in early-stage lung cancer patients. The objective of this study was to develop a radiomics model for computer-assisted detection of local recurrence and fibrosis for an earlier timepoint (<1 year) after the SABR treatment.Approach. This retrospective clinical study included CT images (n= 107) of 66 patients treated with SABR. A z-score normalization technique was used for radiomic feature standardization across scanner protocols. The training set for the radiomics model consisted of CT images (66 patients; 22 recurrences and 44 fibrosis) obtained at 24 months (median) follow-up. The test set included CT-images of 41 patients acquired at 5-12 months follow-up. Combinations of four widely used machine learning techniques (support vector machines, gradient boosting, random forests (RF), and logistic regression) and feature selection methods (Relief feature scoring, maximum relevance minimum redundancy, mutual information maximization, forward feature selection, and LASSO) were investigated. Pyradiomics was used to extract 106 radiomic features from the CT-images for feature selection and classification.Main results. An RF + LASSO model scored the highest in terms of AUC (0.87) and obtained a sensitivity of 75% and a specificity of 88% in identifying a local recurrence in the test set. In the training set, 86% accuracy was achieved using five-fold cross-validation. Delong's test indicated that AUC achieved by the RF+LASSO is significantly better than 11 other machine learning models presented here. The top three radiomic features: interquartile range (first order), Cluster Prominence (GLCM), and Autocorrelation (GLCM), were revealed as differentiating a recurrence from fibrosis with this model.Significance. The radiomics model selected, out of multiple machine learning and feature selection algorithms, was able to differentiate a recurrence from fibrosis in earlier follow-up CT-images with a high specificity rate and satisfactory sensitivity performance.

Iterative image reconstruction with polar coordinate discretized system matrix for optical CT radiochromic gel dosimetry.

BACKGROUND: Gel dosimeters are a potential tool for measuring the complex dose distributions that characterize modern radiotherapy. A prototype tabletop solid-tank fan-beam optical CT scanner for readout of gel dosimeters was recently developed. This scanner does not have a straight raypath from source to detector, thus images cannot be reconstructed using filtered backprojection (FBP) and iterative techniques are required. Iterative image reconstruction requires a system matrix that describes the geometry of the imaging system. Stored system matrices can become immensely large, making them impractical for storage on a typical desktop computer. PURPOSE: Here we develop a method to reduce the storage size of optical CT system matrices through use of polar coordinate discretization while accounting for the refraction in optical CT systems. METHODS: A ray tracing simulator was developed to track the path of light rays as they traverse the different mediums of the optical CT scanner. Cartesian coordinate discretized system matrices (CCDSMs) and polar coordinate discretized system matrices (PCDSMs) were generated by discretizing the reconstruction area of the optical CT scanner into a Cartesian pixel grid and a polar coordinate pixel grid, respectively. The length of each ray through each pixel was calculated and used to populate the system matrices. To ensure equal weighting during iterative reconstruction, the radial rings of PCDSMs were asymmetrically spaced such that the area of each polar pixel was constant. Two clinical phantoms and several synthetic phantoms were produced and used to evaluate the reconstruction techniques under known conditions. Reconstructed images were analyzed in terms of spatial resolution, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal nonuniformity (SNU), and Gamma map pass percentage. RESULTS: A storage size reduction of 99.72% was found when comparing a PCDSM to a CCDSM with the same total number of pixels. Images reconstructed with a PCDSM were found to have superior SNR, CNR, SNU, and Gamma (1 mm, 1%) pass percentage compared to those reconstructed with a CCDSM. Increasing spatial resolution in the radial direction with increasing radial distance was found in both PCDSM and CCDSM reconstructions due to the outer regions refracting light more severely. Images reconstructed with a PCDSM showed a decrease in spatial resolution in the azimuthal directions as radial distance increases, due to the widening of the polar pixels. However, this can be mitigated with only a slight increase in storage size by increasing the number of projections. A loss of spatial resolution in the radial direction within 5 mm radially from center was found when reconstructing with a PCDSM, due to the large innermost pixels. However, this was remedied by increasing the number of radial rings within the PCDSM, yielding radial spatial resolution on par with images reconstructed with a CCDSM and a storage size reduction of 99.26%. CONCLUSIONS: Discretizing the image pixel elements in polar coordinates achieved a system matrix storage size reduction of 99.26% with only minimal reduction in the image quality.

Reconstruction of Raman Spectra of Biochemical Mixtures Using Group and Basis Restricted Non-Negative Matrix Factorization.

Raman spectroscopy is a useful tool for obtaining biochemical information from biological samples. However, interpretation of Raman spectroscopy data in order to draw meaningful conclusions related to the biochemical make up of cells and tissues is often difficult and could be misleading if care is not taken in the deconstruction of the spectral data. Our group has previously demonstrated the implementation of a group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework as an alternative to more widely used dimensionality reduction techniques such as principal component analysis (PCA) for the deconstruction of Raman spectroscopy data as related to radiation response monitoring in both cellular and tissue data. While this method provides better biological interpretability of the Raman spectroscopy data, there are some important factors which must be considered in order to provide the most robust GBR-NMF model. We here evaluate and compare the accuracy of a GBR-NMF model in the reconstruction of three mixture solutions of known concentrations. The factors assessed include the effect of solid versus solutions bases spectra, the number of unconstrained components used in the model, the tolerance of different signal to noise thresholds, and how different groups of biochemicals compare to each other. The robustness of the model was assessed by how well the relative concentration of each individual biochemical in the solution mixture is reflected in the GBR-NMF scores obtained. We also evaluated how well the model can reconstruct original data, both with and without the inclusion of an unconstrained component. Overall, we found that solid bases spectra were generally comparable to solution bases spectra in the GBR-NMF model for all groups of biochemicals. The model was found to be relatively tolerant of high levels of noise in the mixture solutions using solid bases spectra. Additionally, the inclusion of an unconstrained component did not have a significant effect on the deconstruction, on the condition that all biochemicals in the mixture were included as bases chemicals in the model. We also report that some groups of biochemicals achieve a more accurate deconstruction using GBR-NMF than others, likely due to similarity in the individual bases spectra.

Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts.

Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.

Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity.

OBJECTIVE: In this work, we explore and develop a method that uses Raman spectroscopy to measure and differentiate radiation induced toxicity in murine lungs with the goal of setting the foundation for a predictive disease model. METHODS: Analysis of Raman tissue data is achieved through a combination of techniques. We first distinguish between tissue measurements and air pockets in the lung by using group and basis restricted non-negative matrix factorization. We then analyze the tissue spectra using sparse multinomial logistic regression to discriminate between fibrotic gradings. Model validation is achieved by splitting the data into a training set containing 70% of the data and a test set with the remaining 30%; classification accuracy is used as the performance metric. We also explore several other potential classification tasks wherein the response considered is the grade of pneumonitis and fibrosis sickness. RESULTS: A classification accuracy of 91.6% is achieved on the test set of fibrotic gradings, illustrating the ability of Raman measurements to detect differing levels of fibrotic disease among the murine lungs. It is also shown via further modeling that coarser consideration of fibrotic grading via binning (ie. 'Low', 'Medium', 'High') does not degrade performance. Finally, we consider preliminary models for pneumonitis discrimination using the same methodologies.

Radiation treatment response and hypoxia biomarkers revealed by machine learning assisted Raman spectroscopy in tumour cells and xenograft tissues.

Recent advancements in anatomical imaging of tumours as treatment targets have led to improvements in RT. However, it is unlikely that improved anatomical imaging alone will be the sole driver for new advances in personalised RT. Biochemically based radiobiological information is likely to be required for next-generation improvements in the personalisation of radiotherapy dose prescriptions to individual patients. In this paper, we use Raman spectroscopy (RS), an optical technique, to monitor individual biochemical response to radiation within a tumour microenvironment. We spatially correlate individual biochemical responses to augmentatively derived hypoxic maps within the tumour microenvironment. Furthermore, we pair RS with a data analytical framework combining (i) group and basis restricted non-negative matrix factorization (GBR-NMF), (ii) a random forest (RF) classifier, (iii) and a feature metric importance calculation method, Shapley Additive exPlanations (SHAP), in order to ascertain the relative importance of individual biochemicals in describing the overall biological response as observed with RS. The current study found that the GBR-NMF-RF-SHAP model helped identify a wide range of radiation response biomarkers and hypoxia indicators (e.g., glycogen, lipids, DNA, amino acids) in H460 human lung cancer cells and H460 xenografts. Correlations between the hypoxic regions and Raman chemical biomarkers (e.g., glycogen, alanine, and arginine) were also identified in H460 xenografts. To summarize, GBR-NMF-RF-SHAP combined with RS can be applied to monitor the RT-induced biochemical response within cellular and tissue environments. Individual biochemicals were identified that (i) contributed to overall biological response to radiation, and (ii) spatially correlated with hypoxic regions of the tumour. RS combined with our analytical pipeline shows promise for further understanding of individual biochemical dynamics in radiation response for use in cancer therapy.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study.

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer.

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

Group and Basis Restricted Non-Negative Matrix Factorization and Random Forest for Molecular Histotype Classification and Raman Biomarker Monitoring in Breast Cancer.

Raman spectroscopy is a non-invasive optical technique that can be used to investigate biochemical information embedded in cells and tissues exposed to ionizing radiation used in cancer therapy. Raman spectroscopy could potentially be incorporated in personalized radiation treatment design as a tool to monitor radiation response in at the metabolic level. However, tracking biochemical dynamics remains challenging for Raman spectroscopy. Here we developed a novel analytical framework by combining group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF). This framework can monitor radiation response profiles in different molecular histotypes and biochemical dynamics in irradiated breast cancer cells. Five subtypes of; human breast cancer (MCF-7, BT-474, MDA-MB-230, and SK-BR-3) and normal cells derived from human breast tissue (MCF10A) which had been exposed to ionizing radiation were tested in this framework. Reference Raman spectra of 20 biochemicals were collected and used as the constrained Raman biomarkers in the GBR-NMF-RF framework. We obtained scores for individual biochemicals corresponding to the contribution of each Raman reference spectrum to each spectrum obtained from the five cell types. A random forest classifier was then fitted to the chemical scores for performing molecular histotype classifications (HER2, PR, ER, Ki67, and cancer versus non-cancer) and assessing the importance of the Raman biochemical basis spectra for each classification test. Overall, the GBR-NMF-RF framework yields classification results with high accuracy (>97%), high sensitivity (>97%), and high specificity (>97%). Variable importance calculated in the random forest model indicated high contributions from glycogen and lipids (cholesterol, phosphatidylserine, and stearic acid) in molecular histotype classifications.

Raman spectroscopy and group and basis-restricted non negative matrix factorisation identifies radiation induced metabolic changes in human cancer cells.

This work combines single cell Raman spectroscopy (RS) with group and basis restricted non-negative matrix factorisation (GBR-NMF) to identify individual biochemical changes associated with radiation exposure in three human cancer cell lines. The cell lines analysed were derived from lung (H460), breast (MCF7) and prostate (LNCaP) tissue and are known to display varying degrees of radio sensitivity due to the inherent properties of each cell type. The GBR-NMF approach involves the deconstruction of Raman spectra into component biochemical bases using a library of Raman spectra of known biochemicals present in the cells. Subsequently, scores are obtained on each of these bases which can be directly correlated with the contribution of each chemical to the overall Raman spectrum. We validated GBR-NMF through the correlation of GBR-NMF-derived glycogen scores with scores that were previously observed using principal component analysis (PCA). Phosphatidylcholine, glucose, arginine and asparagine showed a distinct differential score pattern between radio-resistant and radio-sensitive cell types. In summary, the GBR-NMF approach allows for the monitoring of individual biochemical radiation-response dynamics previously unattainable with more traditional PCA-based approaches.

Monitor Ionizing Radiation-Induced Cellular Responses with Raman Spectroscopy, Non-Negative Matrix Factorization, and Non-Negative Least Squares.

Radiation therapy (RT) is one of the most commonly prescribed cancer treatments. New tools that can accurately monitor and evaluate individual patient responses would be a major advantage and lend to the implementation of personalized treatment plans. In this study, Raman spectroscopy (RS) was applied to examine radiation-induced cellular responses in H460, MCF7, and LNCaP cancer cell lines across different dose levels and times post-irradiation. Previous Raman data analysis was conducted using principal component analysis (PCA), which showed the ability to extract biological information of glycogen. In the current studies, the use of non-negative matrix factorization (NMF) allowed for the discovery of multiplexed biological information, specifically uncovering glycogen-like and lipid-like component bases. The corresponding scores of glycogen and previously unidentified lipids revealed the content variations of these two chemicals in the cellular data. The NMF decomposed glycogen and lipid-like bases were able to separate the cancer cell lines into radiosensitive and radioresistant groups. A further lipid phenotype investigation was also attempted by applying non-negative least squares (NNLS) to the lipid-like bases decomposed individually from three cell lines. Qualitative differences found in lipid weights for each lipid-like basis suggest the lipid phenotype differences in the three tested cancer cell lines. Collectively, this study demonstrates that the application of NMF and NNLS on RS data analysis to monitor ionizing radiation-induced cellular responses can yield multiplexed biological information on bio-response to RT not revealed by conventional chemometric approaches.

Raman spectroscopy detects metabolic signatures of radiation response and hypoxic fluctuations in non-small cell lung cancer.

BACKGROUND: Radiation therapy is a standard form of treating non-small cell lung cancer, however, local recurrence is a major issue with this type of treatment. A better understanding of the metabolic response to radiation therapy may provide insight into improved approaches for local tumour control. Cyclic hypoxia is a well-established determinant that influences radiation response, though its impact on other metabolic pathways that control radiosensitivity remains unclear. METHODS: We used an established Raman spectroscopic (RS) technique in combination with immunofluorescence staining to measure radiation-induced metabolic responses in human non-small cell lung cancer (NSCLC) tumour xenografts. Tumours were established in NOD.CB17-Prkdcscid/J mice, and were exposed to radiation doses of 15 Gy or left untreated. Tumours were harvested at 2 h, 1, 3 and 10 days post irradiation. RESULTS: We report that xenografted NSCLC tumours demonstrate rapid and stable metabolic changes, following exposure to 15 Gy radiation doses, which can be measured by RS and are dictated by the extent of local tissue oxygenation. In particular, fluctuations in tissue glycogen content were observed as early as 2 h and as late as 10 days post irradiation. Metabolically, this signature was correlated to the extent of tumour regression. Immunofluorescence staining for γ-H2AX, pimonidazole and carbonic anhydrase IX (CAIX) correlated with RS-identified metabolic changes in hypoxia and reoxygenation following radiation exposure. CONCLUSION: Our results indicate that RS can identify sequential changes in hypoxia and tumour reoxygenation in NSCLC, that play crucial roles in radiosensitivity.

Haralick texture feature analysis for quantifying radiation response heterogeneity in murine models observed using Raman spectroscopic mapping.

Tumour heterogeneity plays a large role in the response of tumour tissues to radiation therapy. Inherent biological, physical, and even dose deposition heterogeneity all play a role in the resultant observed response. We here implement the use of Haralick textural analysis to quantify the observed glycogen production response, as observed via Raman spectroscopic mapping, of tumours irradiated within a murine model. While an array of over 20 Haralick features have been proposed, we here concentrate on five of the most prominent features: homogeneity, local homogeneity, contrast, entropy, and correlation. We show that these Haralick features can be used to quantify the inherent heterogeneity of the Raman spectroscopic maps of tumour response to radiation. Furthermore, our results indicate that Haralick-calculated textural features show a statistically significant dose dependent variation in response heterogeneity, specifically, in glycogen production in tumours irradiated with clinically relevant doses of ionizing radiation. These results indicate that Haralick textural analysis provides a quantitative methodology for understanding the response of murine tumours to radiation therapy. Future work in this area can, for example, utilize the Haralick textural features for understanding the heterogeneity of radiation response as measured by biopsied patient tumour samples, which remains the standard of patient tumour investigation.

Delivered Dose Distribution Visualized Directly With Onboard kV-CBCT: Proof of Principle.

PURPOSE: To demonstrate proof of principle of visualizing delivered 3-dimensional (3D) dose distribution using kilovoltage (kv) cone beam computed tomography (CBCT) mounted onboard a linear accelerator. We apply this technique as a unique end-to-end verification of multifocal radiosurgery where the coincidence of radiation and imaging systems is quantified comprehensively at all targets. METHODS AND MATERIALS: Dosimeters (9.5-cm diameter N-isopropylacrylamide) were prepared according to standard procedures at one facility and shipped to a second (remote) facility for irradiation. A 4-arc volumetric modulated arc therapy (VMAT) multifocal radiosurgery plan was prepared to deliver 20 Gy with 6-MV photons to 6 targets (1-cm diameter). A dosimeter was aligned via CBCT and irradiated, followed by 3 CBCT scans acquired immediately, with total time between pre-CBCT and final CBCT <30 minutes. Image processing included background subtraction and low-pass filters. A dose-volume structure was created per target with the same volume as the planned prescription dose volume, and their spatial agreement was quantified using volume centroid and the Jaccard index. For comparison, 5 diagnostic computed tomography (CT) scans were also acquired after >24 hours with the same spatial analysis applied; comparison with planned doses after absolute dose calibration also was conducted. RESULTS: Regions of high dose were clearly visualized in the average CBCT with a contrast-to-noise ratio of 1.7 ± 0.7, which increased to 5.8 ± 0.5 after image processing, and 11.9 ± 3.7 for average diagnostic CT. Centroids of prescription isodose volumes agreed with the root mean square difference of 1.1 mm (range, 0.8-1.7 mm) for CBCT and 0.7 mm (0.4-0.8 mm) for diagnostic CT. The dose was proportional to density above 10 to 12 Gy with a 3D gamma pass rate of 94.0% and 99.5% using 5% for 1-mm and 3% for 2-mm criteria, respectively (threshold = 15 Gy, using global dose criteria). CONCLUSIONS: This work demonstrates for the first time the potential to visualize in 3D delivered dose using onboard kV-CBCT (0.5 × 0.5 × 1 mm3 voxel size) immediately after irradiation with a sufficient contrast-to-noise ratio to measure radiation and imaging system coincidence to within 2 mm.

Breast cancer subtype specific biochemical responses to radiation.

External beam radiotherapy is a common form of treatment for breast cancer. Among patients and across different breast cancer subtypes, the response to radiation is heterogeneous. Radiation-induced biochemical changes were examined by Raman spectroscopy using cell lines that represent a spectrum of human breast cancer. Principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) revealed unique Raman spectral features in the HER2 and Ki67 subtype. The changes in Raman spectral profiles to different doses of radiation (0-50 Gy) included variations in the levels of proteins, lipids, nucleic acids and glycogen. Importantly, the differences in radiation-induced changes on the normal breast epithelial cell line MCF10A could be discriminated within and across the various breast tumor cell lines. These results demonstrate a novel approach to uncover differences between breast cancer cell subtypes and surrounding normal tissues by their biochemical variations in response to radiation.

Raman Spectroscopic Signatures Reveal Distinct Biochemical and Temporal Changes in Irradiated Human Breast Adenocarcinoma Xenografts.

Radiation therapy plays a crucial role in the management of breast cancer. However, current standards of care have yet to accommodate patient-specific radiation sensitivity. Raman spectroscopy is promising for applications in radiobiological studies and as a technique for personalized radiation oncology, since it can detect spectral changes in irradiated tissues. In this study, we used established Raman spectroscopic approaches to investigate the biochemical nature and temporal evolution of spectral changes in human breast adenocarcinoma xenografts after in vivo irradiation. Spectral alterations related to cell cycle variations with radiation dose were identified for tumors treated using a range of single-fraction ionizing radiation doses. Additional dose-dependent spectral changes linked to specific proteins, nucleic acids and lipids were also identified in irradiated tumors with a clear temporal evolution of the expression of these signatures. This study reveals distinct shifts in Raman spectra after in vivo irradiation of human breast adenocarcinoma xenografts, emphasizing the significance of Raman spectroscopy for assessing tumor response during radiation therapy.