Deciphering oxygen distribution and hypoxia profiles in the tumor microenvironment: a data-driven mechanistic modeling approach.

Objective. The distribution of hypoxia within tissues plays a critical role in tumor diagnosis and prognosis. Recognizing the significance of tumor oxygenation and hypoxia gradients, we introduce mathematical frameworks grounded in mechanistic modeling approaches for their quantitative assessment within a tumor microenvironment. By utilizing known blood vasculature, we aim to predict hypoxia levels across different tumor types.Approach. Our approach offers a computational method to measure and predict hypoxia using known blood vasculature. By formulating a reaction-diffusion model for oxygen distribution, we derive the corresponding hypoxia profile.Main results. The framework successfully replicates observed inter- and intra-tumor heterogeneity in experimentally obtained hypoxia profiles across various tumor types (breast, ovarian, pancreatic). Additionally, we propose a data-driven method to deduce partial differential equation models with spatially dependent parameters, which allows us to comprehend the variability of hypoxia profiles within tissues. The versatility of our framework lies in capturing diverse and dynamic behaviors of tumor oxygenation, as well as categorizing states of vascularization based on the dynamics of oxygen molecules, as identified by the model parameters.Significance. The proposed data-informed mechanistic method quantitatively assesses hypoxia in the tumor microenvironment by integrating diverse histopathological data and making predictions across different types of data. The framework provides valuable insights from both modeling and biological perspectives, advancing our comprehension of spatio-temporal dynamics of tumor oxygenation.

Comparison of semi-automated and manual methods to measure the volume of prostate cancer on magnetic resonance imaging.

PURPOSE: The purpose of this study was to determine the accuracy of manual semi-automated and volumetric measurements to assess prostate cancer volume on multiparametric magnetic resonance imaging (MP-MRI) using whole-mount histopathology for validation. MATERIALS AND METHODS: We evaluated 30 consecutive men (median age, 65.7 years; interquartile range [IQR], 61.5-70.9 years) with a median prostatic specific antigen of 8.5ng/dL (IQR, 5.5-10.5ng/dL), who underwent MP-MRI before radical prostatectomy. Index tumor volume was determined prospectively and independently on the basis of MRI and whole-mount section volumetric assessment using the maximum histologic diameter (MHD) and the histologic volume (HV). The MRI index tumor volume was determined by two independent radiologists using a single measurement of the maximum tumor dimension (MTD), a simplified MR ellipsoid volume (MREV) calculation and a MR region of interest volume (MROV) segmentation displayed by a commercially available OsiriX®. MTD was compared to MHD, whereas MREV and MROV were compared to HV. RESULTS: Thirty index lesions (median HV, 1.514 cm3; IQR, 0.05-3.780 cm3) were analyzed. The MREV, MROV and HD were significantly correlated with each other (r>0.5). Inter-observer agreement for measurements was good for each method (r>0.780). The MTD was the best predictor of maximum histologic diameter (r=0.980 and 0.791) and had an excellent inter-variability correlation (P<0.0001). CONCLUSION: Prostate cancer histologic volume can be assessed using MREV or MROV with a good accuracy and low inter-observer variability. MTD has the lowest inter-observer variability and provides best degrees of correlation with MHD. MTD should be used on MRI for selecting and following patients for active surveillance and staging before focal treatment of prostate cancer.

Towards semantic-driven high-content image analysis: an operational instantiation for mitosis detection in digital histopathology.

This study concerns a novel symbolic cognitive vision framework emerged from the Cognitive Microscopy (MICO(1)) initiative. MICO aims at supporting the evolution towards digital pathology, by studying cognitive clinical-compliant protocols involving routine virtual microscopy. We instantiate this paradigm in the case of mitotic count as a component of breast cancer grading in histopathology. The key concept of our approach is the role of the semantics as driver of the whole slide image analysis protocol. All the decisions being taken into a semantic and formal world, MICO represents a knowledge-driven platform for digital histopathology. Therefore, the core of this initiative is the knowledge representation and the reasoning. Pathologists' knowledge and strategies are used to efficiently guide image analysis algorithms. In this sense, hard-coded knowledge, semantic and usability gaps are to be reduced by a leading, active role of reasoning and of semantic approaches. Integrating ontologies and reasoning in confluence with modular imaging algorithms, allows the emergence of new clinical-compliant protocols for digital pathology. This represents a promising way to solve decision reproducibility and traceability issues in digital histopathology, while increasing the flexibility of the platform and pathologists' acceptance, the one always having the legal responsibility in the diagnosis process. The proposed protocols open the way to increasingly reliable cancer assessment (i.e. multiple slides per sample analysis), quantifiable and traceable second opinion for cancer grading, and modern capabilities for cancer research support in histopathology (i.e. content and context-based indexing and retrieval). Last, but not least, the generic approach introduced here is applicable for number of additional challenges, related to molecular imaging and, in general, to high-content image exploration.

Multispectral band selection and spatial characterization: Application to mitosis detection in breast cancer histopathology.

Breast cancer is the second most frequent cancer. The reference process for breast cancer prognosis is Nottingham grading system. According to this system, mitosis detection is one of the three important criteria required for grading process and quantifying the locality and prognosis of a tumor. Multispectral imaging, as relatively new to the field of histopathology, has the advantage, over traditional RGB imaging, to capture spectrally resolved information at specific frequencies, across the electromagnetic spectrum. This study aims at evaluating the accuracy of mitosis detection on histopathological multispectral images. The proposed framework includes: selection of spectral bands and focal planes, detection of candidate mitotic regions and computation of morphological and multispectral statistical features. A state-of-the-art of the methods for mitosis classification is also provided. This framework has been evaluated on MITOS multispectral dataset and achieved higher detection rate (67.35%) and F-Measure (63.74%) than the best MITOS contest results (Roux et al., 2013). Our results indicate that the selected multispectral bands have more discriminant information than a single spectral band or all spectral bands for mitotic figures, validating the interest of using multispectral images to improve the quality of the diagnostic in histopathology.