Identification of self-incompatibility alleles in Quince (Cydonia oblonga Mill.).

The Quince (Cydonia oblonga Mill.), typically known for its self-compatibility, surprisingly presents a degree of self-incompatibility. This research focused on exploring the diversity within the self-incompatibility gene locus (S) in various C. oblonga genotypes. Through meticulous DNA sequencing, the study sought to unearth potential novel S alleles. In the process of genotyping the S gene across multiple quince genotypes, not only were the previously documented S1 and S2 alleles identified, but this investigation also uncovered two previously unrecognized alleles, termed S4 and S5. These alleles, particularly S4, emerged as the most prevalent among the tested genotypes. To corroborate the findings derived from DNA sequencing, the study employed pollen tube growth germination assays. These assays elucidated a higher pollen germination rate in the Ardabil2 genotype in contrast to Behta. Additionally, the study involved assessing pollen tube growth in both Ardabil2 and Behta through cross-pollination techniques, meticulously tracking the development of pollen tubes at various stages. Remarkably, the outcomes demonstrated that the Behta genotype possesses self-incompatibility, whereas the Ardabil2 genotype showcases a notable degree of self-compatibility. This groundbreaking discovery of new S alleles in quince not only affirms the species' self-compatibility but also sheds light on the complexities of allelic diversity and its impact on self-incompatibility. Such insights are invaluable for enhancing the yield of quince orchards through strategic breeding programs.

Multivariate curve resolution-soft independent modelling of class analogy (MCR-SIMCA).

BACKGROUND: Various classification, class modeling, and clustering techniques operate within abstract spaces, utilizing Principal Components (e.g., Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA)) or latent variable spaces (e.g., Partial Least Squares Discriminant Analysis (PLS-DA)). It's important to note that PCA, despite being a mathematical tool, defines its Principal Components under certain mathematical constraints, it has a wide range of applications in the analysis of real-world systems. In this research, we assess the viability of employing the Multivariate Curve Resolution (MCR) subspace within class modeling techniques, as an alternative to the PC subspace. (92). RESULTS: This study evaluates the use of the MCR subspace in class modeling methods, specifically in tandem with soft independent modeling of class analogy (SIMCA), to investigate the advantages of employing the meaningful physico-chemical subspace of MCR over the mathematical subspace of PCA. In the MCR-SIMCA strategy, the model is constructed by applying MCR to training samples from a target class. The MCR model effectively partitions the data into two smaller sub-matrices: the contribution matrix and the corresponding response matrix. In the next step, the contribution matrix resulting from the decomposition of the training set develops a distance plot (DP). First, the theory of the MCR-SIMCA model is discussed in detail. Next, two real experimental datasets were analyzed, and their performance was compared with the DD-SIMCA model. In most cases, the results were as good as or even more satisfactory than those obtained with the DD-SIMCA model. (146). SIGNIFICANCE: The suggested class modeling method presents a promising avenue for the analysis of real-world natural systems. The study's results emphasize the practical utility of the MCR approach, underscoring the significance of the MCR subspace advantages over the PCA subspace. (39).

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study.

Purpose.This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients.Methods.A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity.Results.A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5-1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5-2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT.Conclusion.Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.

Radiomics based predictive modeling of rectal toxicity in prostate cancer patients undergoing radiotherapy: CT and MRI comparison.

BACKGROUND: Rectal toxicity is one of the common side effects after radiotherapy in prostate cancer patients. Radiomics is a non-invasive and low-cost method for developing models of predicting radiation toxicity that does not have the limitations of previous methods. These models have been developed using individual patients' information and have reliable and acceptable performance. This study was conducted by evaluating the radiomic features of computed tomography (CT) and magnetic resonance (MR) images and using machine learning (ML) methods to predict radiation-induced rectal toxicity. METHODS: Seventy men with pathologically confirmed prostate cancer, eligible for three-dimensional radiation therapy (3DCRT) participated in this prospective trial. Rectal wall CT and MR images were used to extract first-order, shape-based, and textural features. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Classifiers such as Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and K-Nearest Neighbors (KNN) were used to create models based on radiomic, dosimetric, and clinical data alone or in combination. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess each model's performance. RESULTS: The best outcomes were achieved by the radiomic features of MR images in conjunction with clinical and dosimetric data, with a mean of AUC: 0.79, accuracy: 77.75%, specificity: 82.15%, and sensitivity: 67%. CONCLUSIONS: This research showed that as radiomic signatures for predicting radiation-induced rectal toxicity, MR images outperform CT images.

Non-target ROIMCR LC-MS analysis of the disruptive effects of TBT over time on the lipidomics of Daphnia magna.

INTRODUCTION: This study has investigated the temporal disruptive effects of tributyltin (TBT) on lipid homeostasis in Daphnia magna. To achieve this, the study used Liquid Chromatography-Mass Spectrometry (LC-MS) analysis to analyze biological samples of Daphnia magna treated with TBT over time. The resulting data sets were multivariate and three-way, and were modeled using bilinear and trilinear non-negative factor decomposition chemometric methods. These methods allowed for the identification of specific patterns in the data and provided insight into the effects of TBT on lipid homeostasis in Daphnia magna. OBJECTIVES: Investigation of how are the changes in the lipid concentrations of Daphnia magna pools when they were exposed with TBT and over time using non-targeted LC-MS and advanced chemometric analysis. METHODS: The simultaneous analysis of LC-MS data sets of Daphnia magna samples under different experimental conditions (TBT dose and time) were analyzed using the ROIMCR method, which allows the resolution of the elution and mass spectra profiles of a large number of endogenous lipids. Changes obtained in the peak areas of the elution profiles of these lipids caused by the dose of TBT treatment and the time after its exposure are analyzed by principal component analysis, multivariate curve resolution-alternative least square, two-way ANOVA and ANOVA-simultaneous component analysis. RESULTS: 87 lipids were identified. Some of these lipids are proposed as Daphnia magna lipidomic biomarkers of the effects produced by the two considered factors (time and dose) and by their interaction. A reproducible multiplicative effect between these two factors is confirmed and the optimal approach to model this dataset resulted to be the application of the trilinear factor decomposition model. CONCLUSION: The proposed non-targeted LC-MS lipidomics approach resulted to be a powerful tool to investigate the effects of the two factors on the Daphnia magna lipidome using chemometric methods based on bilinear and trilinear factor decomposition models, according to the type of interaction between the design factors.

Spectrophotometric thermodynamic analysis of methylene blue aggregation in water by chemometrics; what comes in the presence of different cyclodextrins?

Spectrophotometry has been utilized to characterize the thermodynamic/dynamic properties of self-aggregation of methylene blue (MB) in water, particularly while interacting with a modulator like different cyclodextrins (α-, ß-, hydroxypropyl-ß- (HP-ß-), and γ-CDs). These systems comprise many interactions that make such chemical systems sophisticated. We developed a mathematical modeling-fitting analysis for the simultaneous quantitative analysis of thermodynamic parameters of chemical reactions, relying on the fitting algorithm. Through analyzing simulated photometric titration data, we demonstrate the simultaneous determination of thermodynamic parameters of the different guest/host interactions. This first has brought the need for the calculation of the visible-light absorption spectrum and the thermodynamic parameters for the pure dimerization system. Therefore, the multiwavelength spectral-mole ratio data of aqueous solutions of MB over a concentration range of 2.5 × 10-5 to 4.5 × 10-5 M while temperature is changing; or being titrated with CDs solutions at various temperatures were collected, augmented, and then have been fed to solid mathematical routines to determine the potential existence of dimeric aggregates. The results of thermodynamics indicated that the positions of the monomer/dimer equilibria do not alter by the presence of α-CD. The apparent dimerization was suppressed upon addition of ß- or HP-ß-CDs, while the addition of γ-CD enhanced the dimerization.

Radiomics and dosiomics-based prediction of radiotherapy-induced xerostomia in head and neck cancer patients.

BACKGROUND AND AIM: Dose-response modeling for radiotherapy-induced xerostomia in head and neck cancer (HN) patients is a promising frontier for personalized therapy. Feature extraction from diagnostic and therapeutic images (radiomics and dosiomics features) can be used for data-driven response modeling. The aim of this study is to develop xerostomia predictive models based on radiomics-dosiomics features. METHODS: Data from the cancer imaging archive (TCIA) for 31 HN cancer patients were employed. For all patients, parotid CT radiomics features were extracted, utilizing Lasso regression for feature selection and multivariate modeling. The models were developed by selected features from pretreatment (CT1), mid-treatment (CT2), post-treatment (CT3), and delta features (ΔCT2-1, ΔCT3-1, ΔCT3-2). We also considered dosiomics features extracted from the parotid dose distribution images (Dose model). Thus, combination models of radio-dosiomics (CT + dose & ΔCT + dose) were developed. Moreover, clinical, and dose-volume histogram (DVH) models were built. Nested 10-fold cross-validation was used to assess the predictive classification of patients into those with and without xerostomia, and the area under the receiver operative characteristic curve (AUC) was used to compare the predictive power of the models. The sensitivity and accuracy of models also were obtained. RESULTS: In total, 59 parotids were assessed, and 13 models were developed. Our results showed three models with AUC of 0.89 as most predictive, namely ΔCT2-1 + Dose (Sensitivity 0.99, Accuracy 0.94 & Specificity 0.86), CT3 model (Sensitivity 0.96, Accuracy 0.94 & Specificity 0.86) and DVH (Sensitivity 0.93, Accuracy 0.89 & Specificity 0.84). These models were followed by Clinical (AUC 0.89, Sensitivity 0.81, Accuracy 0.97 & Specificity 0.89) and CT2 & Dose (AUC 0.86, Sensitivity 0.97, Accuracy 0.87 & Specificity 0.82). The Dose model (developed by dosiomics features only) had AUC, Sensitivity, Specificity, and Accuracy of 0.72, 0.98, 0.33, and 0.79 respectively. CONCLUSION: Quantitative features extracted from diagnostic imaging during and after radiotherapy alone or in combination with dosiomics markers obtained from dose distribution images can be used for radiotherapy response modeling, opening up prospects for personalization of therapies toward improved therapeutic outcomes.

Radioproteomics modeling of metformin-enhanced radiosensitivity: an animal study.

PURPOSE: Metformin is considered as radiation modulator in both tumors and healthy tissues. Radiomics has the potential to decode biological mechanisms of radiotherapy response. The aim of this study was to apply radiomics analysis in metformin-induced radiosensitivity and finding radioproteomics associations of computed tomography (CT) imaging features and proteins involved in metformin radiosensitivity signaling pathways. MATERIALS AND METHODS: A total of 32 female BALB/c mice were used in this study and were subjected to injection of breast cancer cells. When tumors reached a mean volume of 150 mm3, mice were randomly divided into the four groups including Control, Metformin, Radiation, and Radiation + Metformin. Western blot analysis was performed after treatment to measure expression of proteins including AMPK-alpha, phospho-AMPK-alpha (Thr172), mTOR, phospho-mTOR (Ser2448), phospho-4EBP1 (Thr37/46), phospho-ACC (Ser79), and ß-actin. CT imaging was performed before treatment and at the end of treatment in all groups. Radiomics features extracted from segmented tumors were selected using Elastic-net regression and were assessed in terms of correlation with expression of the proteins. RESULTS: It was observed that proteins including phospho-mTOR, phospho-4EBP1, and mTOR had positive correlations with changes in tumor volumes in days 28, 24, 20, 16, and 12, while tumor volume changes at these days had negative correlations with AMPK-alpha, phospho-AMPK-alpha, and phospho-ACC proteins. Furthermore, median feature had a positive correlation with AMPK-alpha, phospho-ACC, and phospho-AMPK-alpha proteins. Also, Cluster shade feature had positive correlations with mTOR and p-mTOR. On the other hand, LGLZE feature had negative correlations with AMPK-alpha and phospho-AMPK-alpha. CONCLUSION: Radiomics features can decode proteins that involved in response to metformin and radiation, although further studies are warranted to investigate the optimal way to integrate radiomics into biological experiments.

Practical and comparative application of efficient data reduction - Multivariate curve resolution.

The term 'Big Data' has recently attracted much attention in science. Working with big data sets can be both challenging and rewarding. The complexity and big data sets make the analysis difficult to deal with, and the increasing volume of data sets requires the development of new practical methods for their handling. In this contribution, we explored the efficient data reduction-multivariate curve resolution (EDR-MCR) strategy based on the convex hull theory for quantitative and qualitative analysis of large chemical data sets. For the quantitative example, the potential of the EDR-MCR method for selecting a representative calibration set was investigated, and the results were compared with the widely used Kennard-Stone (KS) algorithm. The EDR-MCR strategy strongly limits the number of calibration samples with a high potency of prediction performance. The priority of EDR-MCR over KS is its ability to find informative variables and eliminate redundant features. Moreover, the EDR-MCR strategy was also applied for the qualitative analysis of a large-scale metabolomic data set. The comparable analysis results of EDR-MCR with the region of interest (ROI) method confirmed the ability of this method for quantitative analysis of big mass spectrophotometer data sets.

Multi-Scale Temporal Imaging: From Micro- and Meso- to Macro-scale-time Nuclear Medicine.

Time provides a common frame of reference for understanding different processes of change. Within the context of medical imaging, time has three different time scales to be considered: (i) microtime, (ii) mesotime, and (iii) macrotime, respectively, which span a single imaging session, distinct imaging sessions within a short period, and scans with large time gaps spanning months of even years. There has commonly been greater emphasis on the microtime and mesotime scales in both clinical practice and research, with less focus on questions that are at the macrotime scale.

Mutual information map as a new way for exploring the independence of chemically meaningful solutions in two-component analytical data.

In the present contribution, a new approach based on mutual information (MI) is proposed for exploring the independence of feasible solutions in two component systems. Investigating how independent are different feasible solutions can be a way to bridge the gap between independent component analysis (ICA) and multivariate curve resolution (MCR) approaches and, to the best of our knowledge, has not been investigated before. For this purpose, different chromatographic and hyperspectral imaging (HSI) datasets were simulated, considering different noise levels and different degrees of overlap for two-component systems. Feasible solutions were then calculated by both grid search (GS) and Lawton-Sylvester (LS) plots. MI map which is the plot of MI vs. rotation matrix elements was used to estimate the degree of independence between different solutions. Inspection of the results showed that the different solutions in the feasible bands correspond to different MI values and that those values are lower for spectral profiles (more independent) than for concentration profiles (more dependent) as expected from the duality concept and the opposite is true. In addition, component profiles are found near more dependent solutions for concentration profiles and near less dependent solutions for spectral profiles which is due to the fact that "independence" constraint was applied to the spectral profiles in ICA algorithms. The performance of three well-known ICA algorithms (mean-field independent component analysis (MF-ICA), mutual information-based least dependent component analysis (MILCA) and joint approximate diagonalization of eigenmatrices (JADE)) as well as MCR-alternating least squares (MCR-ALS) were investigated. MI maps showed that the solutions of MF-ICA and MCR-ALS are in the feasible bands but the MILCA and JADE solutions which are just based on the independence maximization are outside the MI maps.

Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning.

PURPOSE: The current study aimed to evaluate the association of endorectal ultrasound (EUS) radiomics features at different denoising filters based on machine learning algorithms and to predict radiotherapy response in locally advanced rectal cancer (LARC) patients. METHODS: The EUS images of forty-three LARC patients, as a predictive biomarker for predicting the treatment response of neoadjuvant chemoradiotherapy (NCRT), were investigated. For despeckling, the EUS images were preprocessed by traditional filters (bilateral, wiener, lee, frost, median, and wavelet filters). The rectal tumors were delineated by two readers separately, and radiomics features were extracted. The least absolute shrinkage and selection operator were used for feature selection. Classifiers including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), random forest, naive Bayes, and decision tree were trained using stratified fivefold cross-validation for model development. The area under the curve (AUC) of the receiver operating characteristic curve followed by accuracy, precision, sensitivity, and specificity were obtained for model performance assessment. RESULTS: The wavelet filter had the best results with means of AUC: 0.83, accuracy: 77.41%, precision: 82.15%, and sensitivity: 79.41%. LR and SVM by having AUC: 0.71 and 0.76; accuracy: 70.0% and 71.5%; precision: 75.0% and 73.0%; sensitivity: 69.8% and 80.2%; and specificity: 70.0% and 60.9% had the highest model's performance, respectively. CONCLUSION: This study demonstrated that the EUS-based radiomics model could serve as pretreatment biomarkers in predicting pathologic features of rectal cancer. The wavelet filter and machine learning methods (LR and SVM) had good results on the EUS images of rectal cancer.

Evidence of an additional centre of apple domestication in Iran, with contributions from the Caucasian crab apple Malus orientalis Uglitzk. to the cultivated apple gene pool.

Divergence processes in crop-wild fruit tree complexes in pivotal regions for plant domestication such as the Caucasus and Iran remain little studied. We investigated anthropogenic and natural divergence processes in apples in these regions using 26 microsatellite markers amplified in 550 wild and cultivated samples. We found two genetically distinct cultivated populations in Iran that are differentiated from Malus domestica, the standard cultivated apple worldwide. Coalescent-based inferences showed that these two cultivated populations originated from specific domestication events of Malus orientalis in Iran. We found evidence of substantial wild-crop and crop-crop gene flow in the Caucasus and Iran, as has been described in apple in Europe. In addition, we identified seven genetically differentiated populations of wild apple (M. orientalis), not introgressed by the cultivated apple. Niche modelling combined with genetic diversity estimates indicated that these wild populations likely resulted from range changes during past glaciations. This study identifies Iran as a key region in the domestication of apple and M. orientalis as an additional contributor to the cultivated apple gene pool. Domestication of the apple tree therefore involved multiple origins of domestication in different geographic locations and substantial crop-wild hybridization, as found in other fruit trees. This study also highlights the impact of climate change on the natural divergence of a wild fruit tree and provides a starting point for apple conservation and breeding programmes in the Caucasus and Iran.

Brachytherapy outcome modeling in cervical cancer patients: A predictive machine learning study on patient-specific clinical, physical and dosimetric parameters.

PURPOSE: To predict clinical response in locally advanced cervical cancer (LACC) patients by a combination of measures, including clinical and brachytherapy parameters and several machine learning (ML) approaches. METHODS: Brachytherapy features such as insertion approaches, source metrics, dosimetric, and clinical measures were used for modeling. Four different ML approaches, including LASSO, Ridge, support vector machine (SVM), and Random Forest (RF), were applied to extracted measures for model development alone or in combination. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristics curve, sensitivity, specificity, and accuracy. Our results were compared with a reference model developed by simple logistic regression applied to three distinct clinical features identified by previous papers. RESULTS: One hundred eleven LACC patients were included. Nine data sets were obtained based on the features, and 36 predictive models were built. In terms of AUC, the model developed using RF applied to dosimetric, physical, and total BT sessions features were found as the most predictive [AUC; 0.82 (0.95 confidence interval (CI); 0.79 -0.93), sensitivity; 0.79, specificity; 0.76, and accuracy; 0.77]. The AUC (0.95 CI), sensitivity, specificity, and accuracy for the reference model were found as 0.56 (0.52 ...0.68), 0.51, 0.51, and 0.48, respectively. Most RF models had significantly better performance than the reference model (Bonferroni corrected p-value < 0.0014). CONCLUSION: Brachytherapy response can be predicted using dosimetric and physical parameters extracted from treatment parameters. Machine learning algorithms, including Random Forest, could play a critical role in such predictive modeling.

Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy.

PURPOSE: To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy. METHODS: Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTVCT , CTVCT ), PET (GTVPET40 , CTVPET40 ), and RFMs (GTVRFM , CTVRFM ,). Intratumoral heterogeneity areas were segmented as GTVPET50-Boost and radiomic boost target volume (RTVBoost ) on PET and RFMs, respectively. GTVCT in homogenous tumors and GTVPET40 in heterogeneous tumors were considered as GTVgold standard (GTVGS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTVRFM with GTVGS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes. RESULTS: Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTVRFM-entropy , GTVRFM-contrast , and GTVRFM-H-correlation with GTVGS , respectively. The linear regression results showed a positive correlation between GTVGS and GTVRFM-entropy (r = 0.98, p < 0.001), between GTVGS and GTVRFM-contrast (r = 0.93, p < 0.001), and between GTVGS and GTVRFM-H-correlation (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTVRFM-entropy and CTVRFM-contrast with CTVGS , respectively. Moreover, we used RFM to determine RTVBoost in the heterogeneous tumors. Comparison of RTVBoost with GTVPET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTVBoost-entropy , RTVBoost-contrast , and RTVBoost-H-correlation , respectively. CONCLUSIONS: FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.

Radiomics-guided radiation therapy: opportunities and challenges.

Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.

BACKGROUND: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. METHODS: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. RESULTS: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. CONCLUSION: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.

Bone radiomics reproducibility: a three-centered study on the impacts of image contrast, edge enhancement, and latitude variations.

This study aims to measure the reproducibility of radiomics features in ankle bone radiography over changes in post-processing parameters including contrast, edge enhancement and latitude. Lateral ankle bone radiographies for sixty patients were obtained from three digital radiology centers. All images were acquired by same image acquisition settings. A two-dimensional region of interest was drawn in any image and 93 features from 6 feature sets including first and second order were extracted. The coefficient of variation (COV) and intraclass correlation coefficient (ICC) were calculated to assess feature reproducibility for each center and among all centers in three scenarios: Adams (Nat Rev Endocrinol 9(1):28, 2013) ten different contrast Brown et al. (J Med Imaging 5(1):011017, 2018) ten different edge enhancement and Hirvasniemi et al. (Osteoarthr Cartilage 27(6):906-914, 2019)  ten different image latitude parameters. Based on ICC analysis, it is observed that 46-100-44% of Histogram, 54-72-42% of GLCM, 43-76-36% of GLDM, 60-90-17% of GLRLM, 33-19-21% of GLSZM and 13-20-0% of NGTDM radiomics features had 90% < ICC < 100% over changes in contrast-edge enhancement-latitude changes respectively. Based on COV, GLRLM was only feature set that 100% of their features had COV ≤ 5% over changes in contrast and edge enhancement. The results presented here, indicating that radiomics features extracted are vulnerable over changes in contrast, edge enhancement and latitude. The most reproducible features that introduced in this study could be used for further clinical decision making.

Predicting the uniqueness of single non-negative profiles estimated by multivariate curve resolution methods.

In many kinds of chemical data, one or more species are unknown and the only efficient way to identify and/or quantify them is by mathematical resolution of the mixture spectra. The major problem with such mathematical decompositions is the possibility of obtaining a range of feasible solutions instead of a unique solution due to insufficient prior information about the system under study. However, even with the minimal non-negativity assumptions, there may be some levels of uniqueness, i.e., full/partial/fractional, in the results of the bilinear decomposition of chemical data which is very important to detect. In this study, a procedure is proposed to predict the uniqueness of the resolved non-negative profiles obtained by MCR-ALS (or analogous methods like NMF, EFA, SIMPLISMA, ITTFA, HELP, etc.). This uniqueness prediction is based on the data-based uniqueness (DBU) theorem and the general rule of uniqueness (GRU) presented in previous studies. The proposed procedure is easy to implement, has no additional computational cost, and is general for different systems with any number of components. Several simulated and experimental datasets containing different numbers of components were used to examine and evaluate the proposed procedure.