Publications by authors named "Benoit Plancoulaine"

Article Synopsis
  • * This study applied digital image analysis (DIA) to analyze whole slide images from 254 ER-positive HER2-negative BC patients, focusing on the prognostic value of ITH indicators, particularly using Haralick's texture metrics for Ki67.
  • * The findings revealed that Ki67 entropy, which assesses the spatial arrangement of tumor cells, is a significant predictor of BC-specific survival, surpassing the traditional proliferation rate evaluations and highlighting the importance of ITH in prognosis.
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Triple-negative breast cancer (TNBC) is particularly challenging due to the weak or absent response to therapeutics and its poor prognosis. The effectiveness of neoadjuvant chemotherapy (NAC) response is strongly influenced by changes in elements of the tumor microenvironment (TME). This work aimed to characterize the residual TME composition in 96 TNBC patients using immunohistochemistry and in situ hybridization techniques and evaluate its prognostic implications for partial responders vs.

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With a high risk of relapse and death, and a poor or absent response to therapeutics, the triple-negative breast cancer (TNBC) subtype is particularly challenging, especially in patients who cannot achieve a pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). Although the tumor microenvironment (TME) is known to influence disease progression and the effectiveness of therapeutics, its predictive and prognostic potential remains uncertain. This work aimed to define the residual TME profile after NAC of a retrospective cohort with 96 TNBC patients by immunohistochemical staining (cell markers) and chromogenic in situ hybridization (genetic markers).

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  • Microstructure studies often struggle with the challenges of microscope lighting, which needs to enhance biological features while avoiding distortion effects.
  • This paper presents a new technique to improve lighting by focusing on joint computation of phase and irradiance rather than using models based on virtual light rays.
  • The study aims to enhance the analysis of biological microstructures while providing additional insights into the computational methods for studying complex arrangements in biological tissues.
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  • The study aims to create a new automated method for quantifying protein biomarkers in high-grade serous ovarian tumors (HGSOC), focusing on a more universal approach rather than arbitrary thresholds.
  • It analyzes a small group of 25 HGSOC cases, calibrating algorithms with expert Stereology analyses to assess immunohistochemical staining in tumor samples.
  • Results indicate a strong correlation between expert assessments and automated image processing, which enhanced understanding of staining heterogeneity and could improve pathologists' decision-making in treatment options.
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Differences between computer-assisted image analysis (CAI) algorithms may cause discrepancies in the identification of immunohistochemically stained immune biomarkers in biopsies of breast cancer patients. These discrepancies have implications for their association with disease outcome. This study aims to compare three CAI procedures (A, B and C) to measure positive marker areas in post-neoadjuvant chemotherapy biopsies of patients with triple-negative breast cancer (TNBC) and to explore the differences in their performance in determining the potential association with relapse in these patients.

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Assessment of intratumoral heterogeneity and tumor-host interaction within the tumor microenvironment is becoming increasingly important for innovative cancer therapy decisions because of the unique information it can generate about the state of the disease. However, its assessment and quantification are limited by ambiguous definitions of the tumor-host interface and by human cognitive capacity in current pathology practice. Advances in machine learning and artificial intelligence have opened the field of digital pathology to novel tissue image analytics and feature extraction for generation of high-capacity computational disease management models.

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Intranuclear birefringent inclusions (IBI) found in various cell types in paraffin-embedded tissue sections have long been considered to be a tissue processing artifact, although an association with biological processes has been suggested. We applied polychromatic polarization microscopy to image their spatial organization. Our study provides evidence that IBI are caused by liquid paraffin-macromolecular crystals formed during paraffin-embedding procedures within cells and potentially reflect an active transcriptional status.

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Background: The axillary lymph nodes (ALNs) in breast cancer patients are the body regions to where tumoral cells most often first disseminate. The tumour immune response is important for breast cancer patient outcome, and some studies have evaluated its involvement in ALN metastasis development. Most studies have focused on the intratumoral immune response, but very few have evaluated the peritumoral immune response.

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Immunohistochemistry (IHC) for ER, PR, HER2, and Ki67 is used to predict outcome and therapy response in breast cancer patients. The current IHC assessment, visual or digital, is based mostly on global biomarker expression levels in the tissue sample. In our study, we explored the prognostic value of digital image analysis of conventional breast cancer IHC biomarkers supplemented with their intratumoral heterogeneity and tissue immune response indicators.

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Tumor cells can modify the immune response in primary tumors and in the axillary lymph nodes with metastasis (ALN) in breast cancer (BC), influencing patient outcome. We investigated whether patterns of immune cells in the primary tumor and in the axillary lymph nodes without metastasis (ALN) differed between patients diagnosed without ALN (diagnosed-ALN) and with ALN (diagnosed-ALN) and the implications for clinical outcome. Eleven immune markers were studied using immunohistochemistry, tissue microarray, and digital image analysis in 141 BC patient samples (75 diagnosed-ALN and 66 diagnosed-ALN).

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This paper investigated whether positron emission tomography (PET) imaging with [F]fludarabine ([F]FDB) can help to differentiate central nervous system lymphoma (CNSL) from glioblastoma (GBM), which is a crucial issue in the diagnosis and management of patients with these aggressive brain tumors. Multimodal analyses with [F]fluorodeoxyglucose ([F]FDG), magnetic resonance imaging (MRI) and histology have also been considered to address the specificity of [F]FDB for CNSL. Nude rats were implanted with human MC116 lymphoma-cells (n = 9) or U87 glioma-cells (n = 4).

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Article Synopsis
  • Gene expression studies have highlighted the need for effective tissue sampling criteria based on immunohistochemistry (IHC) markers, specifically the Ki67 labeling index, for subclassifying breast cancer and advising chemotherapy.
  • The study utilized digital image analysis on 297 breast cancer samples to assess Ki67 expression and evaluated intratumoral heterogeneity, categorizing tumors accordingly.
  • Findings revealed that the number of tissue cores required for accurate biomarker assessment varies; homogeneous tumors needed 5-6 cores while heterogeneous ones needed 11-12 cores to maintain a 10% error margin.
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  • Immunohistochemistry (IHC) is a key tool in pathology used for diagnosing and predicting disease through the detection of specific proteins in tissues, but conventional methods have limitations in accuracy and reproducibility.
  • There’s a growing necessity for objective evaluations of biomarker expression and the ability to analyze spatial differences within tissue samples.
  • The paper discusses the potential for a new approach to IHC that integrates digital technologies, enhancing the quantification and analysis of protein expression to bridge the gap between traditional methods and advanced techniques in pathology.
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Proliferative activity, assessed by Ki67 immunohistochemistry (IHC), is an established prognostic and predictive biomarker of breast cancer (BC). However, it remains under-utilized due to lack of standardized robust measurement methodologies and significant intratumor heterogeneity of expression. A recently proposed methodology for IHC biomarker assessment in whole slide images (WSI), based on systematic subsampling of tissue information extracted by digital image analysis (DIA) into hexagonal tiling arrays, enables computation of a comprehensive set of Ki67 indicators, including intratumor variability.

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Article Synopsis
  • Digital Image Analysis (DIA) improves the accuracy and reproducibility of measuring cell populations in breast cancer tissues by analyzing markers like the Ki67 labeling index, which is essential for prognosis and prediction.
  • A study employed DIA on 302 Ki67-stained breast cancer specimens using whole-slide images and a tumor classifier algorithm to gather data on tumor characteristics, comparing them to traditional methods.
  • The analysis discovered four key factors related to tumor characteristics and enabled the identification of tumor heterogeneity, leading to better visualization of Ki67 activity and the potential improvement of digital immunohistochemistry techniques.
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Background: Currently available microscope slide scanners produce whole slide images at various resolutions from histological sections. Nevertheless, acquisition area and so visualization of large tissue samples are limited by the standardized size of glass slides, used daily in pathology departments. The proposed solution has been developed to build composite virtual slides from images of large tumor fragments.

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Background: Digital image analysis (DIA) enables better reproducibility of immunohistochemistry (IHC) studies. Nevertheless, accuracy of the DIA methods needs to be ensured, demanding production of reference data sets. We have reported on methodology to calibrate DIA for Ki67 IHC in breast cancer tissue based on reference data obtained by stereology grid count.

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Background: Digital immunohistochemistry (IHC) is one of the most promising applications brought by new generation image analysis (IA). While conventional IHC staining quality is monitored by semi-quantitative visual evaluation of tissue controls, IA may require more sensitive measurement. We designed an automated system to digitally monitor IHC multi-tissue controls, based on SQL-level integration of laboratory information system with image and statistical analysis tools.

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Computerized image analysis (IA) can provide quantitative and repeatable object measurements by means of methods such as segmentation, indexation, classification, etc. Embedded in reliable automated systems, IA could help pathologists in their daily work and thus contribute to more accurate determination of prognostic histological factors on whole slide images. One of the key concept pathologists want to dispose of now is a numerical estimation of heterogeneity.

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Background: Cardiac fibrosis disrupts the normal myocardial structure and has a direct impact on heart function and survival. Despite already available digital methods, the pathologist's visual score is still widely considered as ground truth and used as a primary method in histomorphometric evaluations. The aim of this study was to compare the accuracy of digital image analysis tools and the pathologist's visual scoring for evaluating fibrosis in human myocardial biopsies, based on reference data obtained by point counting performed on the same images.

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Introduction: Immunohistochemical Ki67 labelling index (Ki67 LI) reflects proliferative activity and is a potential prognostic/predictive marker of breast cancer. However, its clinical utility is hindered by the lack of standardized measurement methodologies. Besides tissue heterogeneity aspects, the key element of methodology remains accurate estimation of Ki67-stained/counterstained tumour cell profiles.

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Cerebral cavernous malformations (CCMs) are described as vascular lesions consisting of endothelial-lined dilated vessels embedded in a connective tissue sheath without intervening parenchyma between them. Their anatomical connections with the normal blood vessels are still enigmatic and the fine three-dimensional (3-D) organization of these vascular lesions remains to be established. Two stacks of serial histological slices, obtained from two brainstem CCM lesions (from the necropsy of a CCM2 male patient), were stained using Masson's trichrome method and then digitized.

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Lymphatic dissemination is a key event in cervical cancer progression and related tumor lymphatic markers are viewed as promising prognostic factor of nodal extension. However, validating such parameters requires an objective characterization of the lymphatic vasculature. Here, we performed a global analysis of the lymphatic network using a new computerized method applied on whole uterine cervical digital images.

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