LKB1 Loss Correlates with STING Loss and, in Cooperation with ß-Catenin Membranous Loss, Indicates Poor Prognosis in Patients with Operable Non-Small Cell Lung Cancer.

To investigate the incidence and prognostically significant correlations and cooperations of LKB1 loss of expression in non-small cell lung cancer (NSCLC), surgical specimens from 188 metastatic and 60 non-metastatic operable stage I-IIIA NSCLC patients were analyzed to evaluate their expression of LKB1 and pAMPK proteins in relation to various processes. The investigated factors included antitumor immunity response regulators STING and PD-L1; pro-angiogenic, EMT and cell cycle targets, as well as metastasis-related (VEGFC, PDGFRα, PDGFRß, p53, p16, Cyclin D1, ZEB1, CD24) targets; and cell adhesion (ß-catenin) molecules. The protein expression levels were evaluated via immunohistochemistry; the RNA levels of LKB1 and NEDD9 were evaluated via PCR, while KRAS exon 2 and BRAFV600E mutations were evaluated by Sanger sequencing. Overall, loss of LKB1 protein expression was observed in 21% (51/248) patients and correlated significantly with histotype (p < 0.001), KRAS mutations (p < 0.001), KC status (concomitant KRAS mutation and p16 downregulation) (p < 0.001), STING loss (p < 0.001), and high CD24 expression (p < 0.001). STING loss also correlated significantly with loss of LKB1 expression in the metastatic setting both overall (p = 0.014) and in lung adenocarcinomas (LUACs) (p = 0.005). Additionally, LKB1 loss correlated significantly with a lack of or low ß-catenin membranous expression exclusively in LUACs, both independently of the metastatic status (p = 0.019) and in the metastatic setting (p = 0.007). Patients with tumors yielding LKB1 loss and concomitant nonexistent or low ß-catenin membrane expression experienced significantly inferior median overall survival of 20.50 vs. 52.99 months; p < 0.001 as well as significantly greater risk of death (HR: 3.32, 95% c.i.: 1.71-6.43; p <0.001). Our findings underscore the impact of the synergy of LKB1 with STING and ß-catenin in NSCLC, in prognosis.

Multiresolution Self-Supervised Feature Integration via Attention Multiple Instance Learning for Histopathology Analysis.

Digital histopathology image analysis of tumor tissue sections has seen great research interest for automating standard diagnostic tasks, but also for developing novel prognostic biomarkers. However, research has mainly been focused on developing uniresolution models, capturing either high-resolution cellular features or low-resolution tissue architectural features. In addition, in the patch-based weakly-supervised training of deep learning models, the features which represent the intratumoral heterogeneity are lost. In this study, we propose a multiresolution attention-based multiple instance learning framework that can capture cellular and contextual features from the whole tissue for predicting patient-level outcomes. Several basic mathematical operations were examined for integrating multiresolution features, i.e. addition, mean, multiplication and concatenation. The proposed multiplication-based multiresolution model performed the best (AUC=0.864), while all multiresolution models outperformed the uniresolution baseline models (AUC=0.669, 0.713) for breast-cancer grading. (Implementation: https://github.com/tsikup/multiresolution-clam).

New Insights in the Era of Clinical Biomarkers as Potential Predictors of Systemic Therapy-Induced Cardiotoxicity in Women with Breast Cancer: A Systematic Review.

Cardiotoxicity induced by breast cancer therapies is a potentially serious complication associated with the use of various breast cancer therapies. Prediction and better management of cardiotoxicity in patients receiving chemotherapy is of critical importance. However, the management of cancer therapy-related cardiac dysfunction (CTRCD) lacks clinical evidence and is based on limited clinical studies. AIM: To provide an overview of existing and potentially novel biomarkers that possess a promising predictive value for the early and late onset of CTRCD in the clinical setting. METHODS: A systematic review of published studies searching for promising biomarkers for the prediction of CTRCD in patients with breast cancer was undertaken according to PRISMA guidelines. A search strategy was performed using PubMed, Google Scholar, and Scopus for the period 2013-2023. All subjects were >18 years old, diagnosed with breast cancer, and received breast cancer therapies. RESULTS: The most promising biomarkers that can be used for the development of an alternative risk cardiac stratification plan for the prediction and/or early detection of CTRCD in patients with breast cancer were identified. CONCLUSIONS: We highlighted the new insights associated with the use of currently available biomarkers as a standard of care for the management of CTRCD and identified potentially novel clinical biomarkers that could be further investigated as promising predictors of CTRCD.

A segmentation-based method improving the performance of N4 bias field correction on T2weighted MR imaging data of the prostate.

Magnetic Resonance (MR) images suffer from spatial inhomogeneity, known as bias field corruption. The N4ITK filter is a state-of-the-art method used for correcting the bias field to optimize MR-based quantification. In this study, a novel approach is presented to quantitatively evaluate the performance of N4 bias field correction for pelvic prostate imaging. An exploratory analysis, regarding the different values of convergence threshold, shrink factor, fitting level, number of iterations and use of mask, is performed to quantify the performance of N4 filter in pelvic MR images. The performance of a total of 240 different N4 configurations is examined using the Full Width at Half Maximum (FWHM) of the segmented periprostatic fat distribution as evaluation metric. Phantom T2weighted images were used to assess the performance of N4 for a uniform test tissue mimicking material, excluding factors such as patient related susceptibility and anatomy heterogeneity. Moreover, 89 and 204 T2weighted patient images from two public datasets acquired by scanners with a combined surface and endorectal coil at 1.5 T and a surface coil at 3 T, respectively, were utilized and corrected with a variable set of N4 parameters. Furthermore, two external public datasets were used to validate the performance of the N4 filter in T2weighted patient images acquired by various scanning conditions with different magnetic field strengths and coils. The results show that the set of N4 parameters, converging to optimal representations of fat in the image, were: convergence threshold 0.001, shrink factor 2, fitting level 6, number of iterations 100 and the use of default mask for prostate images acquired by a combined surface and endorectal coil at both 1.5 T and 3 T. The corresponding optimal N4 configuration for MR prostate images acquired by a surface coil at 1.5 T or 3 T was: convergence threshold 0.001, shrink factor 2, fitting level 5, number of iterations 25 and the use of default mask. Hence, periprostatic fat segmentation can be used to define the optimal settings for achieving T2weighted prostate images free from bias field corruption to provide robust input for further analysis.

Harmonization Strategies in Multicenter MRI-Based Radiomics.

Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process.

Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution.

The tumor immune microenvironment (TIME) is an important player in breast cancer pathophysiology. Surrogates for antitumor immune response have been explored as predictive biomarkers to immunotherapy, though with several limitations. Immunohistochemistry for programmed death ligand 1 suffers from analytical problems, immune signatures are devoid of spatial information and histopathological evaluation of tumor infiltrating lymphocytes exhibits interobserver variability. Towards improved understanding of the complex interactions in TIME, several emerging multiplex in situ methods are being developed and gaining much attention for protein detection. They enable the simultaneous evaluation of multiple targets in situ, detection of cell densities/subpopulations as well as estimations of functional states of immune infiltrate. Furthermore, they can characterize spatial organization of TIME-by cell-to-cell interaction analyses and the evaluation of distribution within different regions of interest and tissue compartments-while digital imaging and image analysis software allow for reproducibility of the various assays. In this review, we aim to provide an overview of the different multiplex in situ methods used in cancer research with special focus on breast cancer TIME at the neoadjuvant, adjuvant and metastatic setting. Spatial heterogeneity of TIME and importance of longitudinal evaluation of TIME changes under the pressure of therapy and metastatic progression are also addressed.

Pollen Grain Classification Based on Ensemble Transfer Learning on the Cretan Pollen Dataset.

Pollen identification is an important task for the botanical certification of honey. It is performed via thorough microscopic examination of the pollen present in honey; a process called melissopalynology. However, manual examination of the images is hard, time-consuming and subject to inter- and intra-observer variability. In this study, we investigated the applicability of deep learning models for the classification of pollen-grain images into 20 pollen types, based on the Cretan Pollen Dataset. In particular, we applied transfer and ensemble learning methods to achieve an accuracy of 97.5%, a sensitivity of 96.9%, a precision of 97%, an F1 score of 96.89% and an AUC of 0.9995. However, in a preliminary case study, when we applied the best-performing model on honey-based pollen-grain images, we found that it performed poorly; only 0.02 better than random guessing (i.e., an AUC of 0.52). This indicates that the model should be further fine-tuned on honey-based pollen-grain images to increase its effectiveness on such data.

Diffusion Weighted Imaging in the Assessment of Tumor Grade in Endometrial Cancer Based on Intravoxel Incoherent Motion MRI.

The aim of this study is to investigate the possibility of predicting histological grade in patients with endometrial cancer on the basis of intravoxel incoherent motion (IVIM)-related histogram analysis parameters. This prospective study included 52 women with endometrial cancer (EC) who underwent MR imaging as initial staging in our hospital, allocated into low-grade (G1 and G2) and high-grade (G3) tumors according to the pathology reports. Regions of interest (ROIs) were drawn on the diffusion weighted images and apparent diffusion coefficient (ADC), true diffusivity (D), and perfusion fraction (f) using diffusion models were computed. Mean, median, skewness, kurtosis, and interquartile range (IQR) were calculated from the whole-tumor histogram. The IQR of the diffusion coefficient (D) was significantly lower in the low-grade tumors from that of the high-grade group with an adjusted p-value of less than 5% (0.048). The ROC curve analysis results of the statistically significant IQR of the D yielded an accuracy, sensitivity, and specificity of 74.5%, 70.1%, and 76.5% respectively, for discriminating low from high-grade tumors, with an optimal cutoff of 0.206 (×10-3 mm2/s) and an AUC of 75.4% (95% CI: 62.1 to 88.8). The IVIM modeling coupled with histogram analysis techniques is promising for preoperative differentiation between low- and high-grade EC tumors.

Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip.

Differentiation between transient osteoporosis (TOH) and avascular necrosis (AVN) of the hip is a longstanding challenge in musculoskeletal radiology. The purpose of this study was to utilize MRI-based radiomics and machine learning (ML) for accurate differentiation between the two entities. A total of 109 hips with TOH and 104 hips with AVN were retrospectively included. Femoral heads and necks with segmented radiomics features were extracted. Three ML classifiers (XGboost, CatBoost and SVM) using 38 relevant radiomics features were trained on 70% and validated on 30% of the dataset. ML performance was compared to two musculoskeletal radiologists, a general radiologist and two radiology residents. XGboost achieved the best performance with an area under the curve (AUC) of 93.7% (95% CI from 87.7 to 99.8%) among ML models. MSK radiologists achieved an AUC of 90.6% (95% CI from 86.7% to 94.5%) and 88.3% (95% CI from 84% to 92.7%), respectively, similar to residents. The general radiologist achieved an AUC of 84.5% (95% CI from 80% to 89%), significantly lower than of XGboost (p = 0.017). In conclusion, radiomics-based ML achieved a performance similar to MSK radiologists and significantly higher compared to general radiologists in differentiating between TOH and AVN.

Multicenter DSC-MRI-Based Radiomics Predict IDH Mutation in Gliomas.

To address the current lack of dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI)-based radiomics to predict isocitrate dehydrogenase (IDH) mutations in gliomas, we present a multicenter study that featured an independent exploratory set for radiomics model development and external validation using two independent cohorts. The maximum performance of the IDH mutation status prediction on the validation set had an accuracy of 0.544 (Cohen's kappa: 0.145, F1-score: 0.415, area under the curve-AUC: 0.639, sensitivity: 0.733, specificity: 0.491), which significantly improved to an accuracy of 0.706 (Cohen's kappa: 0.282, F1-score: 0.474, AUC: 0.667, sensitivity: 0.6, specificity: 0.736) when dynamic-based standardization of the images was performed prior to the radiomics. Model explainability using local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) revealed potential intuitive correlations between the IDH-wildtype increased heterogeneity and the texture complexity. These results strengthened our hypothesis that DSC-MRI radiogenomics in gliomas hold the potential to provide increased predictive performance from models that generalize well and provide understandable patterns between IDH mutation status and the extracted features toward enabling the clinical translation of radiogenomics in neuro-oncology.

Differentiating low from high-grade soft tissue sarcomas using post-processed imaging parameters derived from multiple DWI models.

PURPOSE: To investigate and histopathologically validate the role of model selection in the design of novel parametric meta-maps towards the discrimination of low from high-grade soft tissue sarcomas (STSs) using multiple Diffusion Weighted Imaging (DWI) models. METHODS: DWI data of 28 patients were quantified using the mono-exponential, bi-exponential, stretched-exponential and the diffusion kurtosis model. Akaike Weights (AW) were calculated from the corrected Akaike Information Criteria (AICc) to select the most suitable model for every pixel within the tumor volume. Pseudo-colorized classification maps were then generated to depict model suitability, hypothesizing that every single model underpins different tissue properties and cannot solely characterize the whole tumor. Single model parametric maps were turned into meta-maps using the classification map and a histological validation of the model suitability results was conducted on several subregions of different tumors. Several histogram metrics were calculated from all derived maps before and after model selection, statistical analysis was conducted using the Mann-Whitney U test, p-values were adjusted for multiple comparisons and performance of all statistically significant metrics was evaluated using the Receiver Operator Characteristic (ROC) analysis. RESULTS: The histologic analysis on several tumor subregions confirmed model suitability results on these areas. Only 3 histogram metrics, all derived from the meta-maps, were found to be statistically significant in differentiating low from high-grade STSs with an AUC higher than 89 %. CONCLUSION: Embedding model selection in the design of the diffusion parametric maps yields to histogram metrics of high discriminatory power in grading STSs.

Extended perfusion protocol for MS lesion quantification.

This study aims to examine a time-extended dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) protocol and report a comparative study with three different pharmacokinetic (PK) models, for accurate determination of subtle blood-brain barrier (BBB) disruption in patients with multiple sclerosis (MS). This time-extended DCE-MRI perfusion protocol, called Snaps, was applied on 24 active demyelinating lesions of 12 MS patients. Statistical analysis was performed for both protocols through three different PK models. The Snaps protocol achieved triple the window time of perfusion observation by extending the magnetic resonance acquisition time by less than 2 min on average for all patients. In addition, the statistical analysis in terms of adj-R 2 goodness of fit demonstrated that the Snaps protocol outperformed the conventional DCE-MRI protocol by detecting 49% more pixels on average. The exclusive pixels identified from the Snaps protocol lie in the low k trans range, potentially reflecting areas with subtle BBB disruption. Finally, the extended Tofts model was found to have the highest fitting accuracy for both analyzed protocols. The previously proposed time-extended DCE protocol, called Snaps, provides additional temporal perfusion information at the expense of a minimal extension of the conventional DCE acquisition time.

Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach.

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.

Perfusion Magnetic Resonance as a Biomarker for Sorafenib-Treated Advanced Hepatocellular Carcinoma: A Pilot Study.

BACKGROUND: Sorafenib is the currently recommended therapy in patients with advanced hepatocellular carcinoma (HCC). Among the several biomarkers available for the evaluation of the therapeutic response and prognosis, there is perfusion magnetic resonance imaging (p-MRI) that, through measurement of the vascular permeability unit (ktrans), may retrieve useful information regarding the microvascular properties of focal liver lesions. The aim of this study was to evaluate the impact of sorafenib therapy in patients with advanced HCC using the p-MRI technique. MATERIALS AND METHODS: In this retrospective study, 27 patients with the diagnosis of advanced HCC were included for palliative therapy using sorafenib. MRI of the liver was performed before the beginning of the oral therapy (T0), after 3 (T3), and after 6 months (T6). Dynamic acquisitions of the tumor (n = 50, during the first 2 min after contrast injection) were obtained in the coronal plane and were used to compute the parametric perfusion maps, acquiring the ktrans value using the extended Tofts pharmacokinetic model. RESULTS: The value of ktrans obtained at T0 was significantly different from the value of ktrans obtained at T6 (p = 0.028). There were no significant differences between T0 and T3 (p = 0.115) or a correlation between ktrans at T0 and the size of the lesion (p = 0.376). The ktrans value at T0 in patients with progression-free survival (PFS) > 6 months was not significantly different from the ktrans value in patients with PFS ≤6 months (p = 0.113). The ktrans value at T0 was not significantly different between patients who were previously submitted to chemoembolization and those who were not submitted (p = 0.587). CONCLUSION: In this pilot study, the ktrans value may serve as a biomarker of tumor response to antiangiogenic therapy, but only 6 months after its initiation. Clinical outcomes such as PFS were not predicted before the initiation of treatment.

INTRODUÇÃO: O sorafenib é a terapêutica atualmente recomendada em doentes com carcinoma hepatocelular avançado. Entre os vários biomarcadores disponíveis para a avaliação da resposta terapêutica e do prognóstico, existe a perfusão por Ressonância Magnética na qual, através da unidade de permeabilidade vascular (ktrans), se obtém informação relativa às propriedades microvasculares das lesões tumorais. O objetivo deste estudo foi avaliar o impacto da terapêutica com sorafenib em doentes com carcinoma hepatocelular avançado, através da técnica de perfusão por Ressonância Magnética (p-RM). MATERIAIS E MÉTODOS: Neste estudo observacional retrospetivo, foram incluídos 27 doentes, com diagnóstico de carcinoma hepatocelular avançado com indicação para terapêutica paliativa com sorafenib. Foi realizado estudo de Ressonância Magnética hepática antes do início da terapêutica com sorafenib (T0), aos 3 (T3) e aos 6 meses (T6) após o seu início. As imagens adquiridas no plano coronal (n = 50, durante os primeiros 2 minutos após a injeção de contraste paramagnético) foram utilizadas para fusão dos mapas paramétricos de perfusão, obtendo-se o valor de ktrans, usando o modelo farmacocinético de Tofts. RESULTADOS: O valor de ktrans obtido em T0 foi significativamente diferente do valor de ktrans obtido em T6 (p = 0.028). Não existiram diferenças significativas entre T0 e T3 (p = 0.115) ou correlação entre o valor de ktrans em T0 e a dimensão da lesão (p = 0.376). Associadamente, o valor de ktrans em T0 nos doentes com sobrevivência livre de progressão superior a 6 meses não foi significativamente diferente do valor de ktrans nos doentes com sobrevivência livre de progressão inferior ou igual a 6 meses (p = 0.113). O valor de ktrans em doentes com ou sem tratamento prévio por quimioembolização não mostrou diferença estatisticamente significativa (p = 0.587). CONCLUSÃO: Neste estudo inicial, o valor de ktrans pode servir como biomarcador da perfusão tumoral na resposta à terapêutica anti-angiogénica, 6 meses após o seu início. O seu valor antes do inicio do tratamento não permitiu predizer o desfecho clinico em termos de sobrevivência livre de doença nos pacientes submetidos ou não a prévia quimioembolização.© 2019 Sociedade Portugueasa de Gastrenterologia.

18 F-NaF PET/CT IMAGING IN FIBROUS DYSPLASIA OF BONE.

Fibrous dysplasia (FD) is a mosaic skeletal disorder resulting in fractures, deformity, and functional impairment. Clinical evaluation has been limited by a lack of surrogate endpoints capable of quantitating disease activity. The purpose of this study was to investigate the utility of 18 F-NaF PET/CT imaging in quantifying disease activity in patients with FD. Fifteen consecutively evaluated subjects underwent whole-body 18 F-NaF PET/CT scans, and FD burden was assessed by quantifying FD-related 18 F-NaF activity. 18 F-NaF PET/CT parameters obtained included (i) SUVmax (standardized uptake value [SUV] of the FD lesion with the highest uptake); (ii) SUVmean (average SUV of all 18 F-NaF-positive FD lesions); (iii) total volume of all 18 F-NaF-positive FD lesions (TV); and (iv) total FD lesion activity determined as the product of TV multiplied by SUVmean (TA = TV × SUVmean ) (TA). Skeletal outcomes, functional outcomes, and bone turnover markers were correlated with 18 F-NaF PET/CT parameters. TV and TA of extracranial FD lesions correlated strongly with skeletal outcomes including fractures and surgeries (p values ≤ 0.003). Subjects with impaired ambulation and scoliosis had significantly higher TV and TA values (P < 0.05), obtained from extracranial and spinal lesions, respectively. Craniofacial surgeries correlated with TV and TA of skull FD lesions (P < 0.001). Bone turnover markers, including alkaline phosphatase, N-telopeptides, and osteocalcin, were strongly correlated with TV and TA (P < 0.05) extracted from FD lesions in the entire skeleton. No associations were identified with SUVmax or SUVmean . Bone pain and age did not correlate with 18 F-NaF PET/CT parameters. FD burden evaluated by 18 F-NaF-PET/CT facilitates accurate assessment of FD activity, and correlates quantitatively with clinically-relevant skeletal outcomes. © 2019 American Society for Bone and Mineral Research.

T2, T2* and spin coupling ratio as biomarkers for the study of lipomatous tumors.

BACKGROUND: Subcutaneous fat may have variable signal intensity on T2w images depending on the choice of imaging parameters. However, fatty components within tumors have a different degree of signal dependence on the acquisition scheme. This study examined the use of T2, T2* relaxometry and spin coupling related signal changes (Spin Coupling ratio, SCr) on two different imaging protocols as clinically relevant descriptors of benign and malignant lipomatous tumors. MATERIALS AND METHODS: 20 patients with benign lipomas or liposarcomas of variable histologic grade were examined at an 1.5 T scanner with Multi Echo Spin Echo (MESE) different echo spacing (ESP) in order to produce bright fat T2w images (ESP: 13.4 ms, 25 equidistant echoes) and dark fat images (ESP: 26.8 ms with 10 equidistant echoes). T2* relaxometry acquisition comprises 4 sets of in-opposed echoes (2.4-19.2 ms, ESP: 2.4 ms) Multi Echo Gradient Echo (MEGRE) sequence. All parametric maps were calculated on a pixel basis. RESULTS: Significant differences of SCr were found for five different types of lipomatous tumors (Pairwise t-test with Bonferroni correction): lipomas, well differentiated liposarcomas, myxoid liposarcomas, pleomorphic liposarcomas and poorly differentiated liposarcomas. SCr surpassed the classification performance of T2 and T2* relaxometry. DATA CONCLUSION: A novel biomarker based on spin coupling related signal loss, SCr, is indicative of lipomatous tumor histological grading. We concluded that T2, T2* and SCr can be used for the classification of fat containing tumors, which may be important for biopsy guidance in heterogeneous masses and treatment planning.

Investigating the Role of Model-Based and Model-Free Imaging Biomarkers as Early Predictors of Neoadjuvant Breast Cancer Therapy Outcome.

Imaging biomarkers (IBs) play a critical role in the clinical management of breast cancer (BRCA) patients throughout the cancer continuum for screening, diagnosis, and therapy assessment, especially in the neoadjuvant setting. However, certain model-based IBs suffer from significant variability due to the complex workflows involved in their computation, whereas model-free IBs have not been properly studied regarding clinical outcome. In this study, IBs from 35 BRCA patients who received neoadjuvant chemotherapy (NAC) were extracted from dynamic contrast-enhanced MR imaging (DCE-MRI) data with two different approaches, a model-free approach based on pattern recognition (PR), and a model-based one using pharmacokinetic compartmental modeling. Our analysis found that both model-free and model-based biomarkers can predict pathological complete response (pCR) after the first cycle of NAC. Overall, eight biomarkers predicted the treatment response after the first cycle of NAC, with statistical significance (p-value < 0.05), and three at the baseline. The best pCR predictors at first follow-up, achieving high AUC and sensitivity and specificity more than 50%, were the hypoxic component with threshold 2 (AUC 90.4%) from the PR method, and the median value of kep (AUC 73.4%) from the model-based approach. Moreover, the 80th percentile of ve achieved the highest pCR prediction at baseline with AUC 78.5%. The results suggest that the model-free DCE-MRI IBs could be a more robust alternative to complex, model-based ones such as kep and favor the hypothesis that the PR image-derived hypoxic image component captures actual tumor hypoxia information able to predict BRCA NAC outcome.

Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation.

Deep learning (DL) architectures have opened new horizons in medical image analysis attaining unprecedented performance in tasks such as tissue classification and segmentation as well as prediction of several clinical outcomes. In this paper, we propose and evaluate a novel three-dimensional (3-D) convolutional neural network (CNN) designed for tissue classification in medical imaging and applied for discriminating between primary and metastatic liver tumors from diffusion weighted MRI (DW-MRI) data. The proposed network consists of four consecutive strided 3-D convolutional layers with 3 × 3 × 3 kernel size and rectified linear unit (ReLU) as activation function, followed by a fully connected layer with 2048 neurons and a Softmax layer for binary classification. A dataset comprising 130 DW-MRI scans was used for the training and validation of the network. To the best of our knowledge this is the first DL solution for the specific clinical problem and the first 3-D CNN for cancer classification operating directly on whole 3-D tomographic data without the need of any preprocessing step such as region cropping, annotating, or detecting regions of interest. The classification performance results, 83% (3-D) versus 69.6% and 65.2% (2-D), demonstrated significant tissue classification accuracy improvement compared to two 2-D CNNs of different architectures also designed for the specific clinical problem with the same dataset. These results suggest that the proposed 3-D CNN architecture can bring significant benefit in DW-MRI liver discrimination and potentially, in numerous other tissue classification problems based on tomographic data, especially in size-limited, disease-specific clinical datasets.

Correction: Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.

[This corrects the article DOI: 10.1371/journal.pone.0184197.].

Corrigendum to "Simulating Radiotherapy Effect in High-Grade Glioma by Using Diffusive Modeling and Brain Atlases".

[This corrects the article DOI: 10.1155/2012/715812.].