Standardization of the tumor-stroma ratio scoring method for breast cancer research

Purpose: The tumor-stroma ratio (TSR) has repeatedly proven to be correlated with patient outcomes in breast cancer using large retrospective cohorts. However, studies validating the TSR often show variability in methodology, thereby hampering comparisons and uniform outcomes. Method: This paper provides a detailed description of a simple and uniform TSR scoring method using Hematoxylin and Eosin (H&E)-stained core biopsies and resection tissue, specifically focused on breast cancer. Possible histological challenges that can be encountered during scoring including suggestions to overcome them are reported. Moreover, the procedure for TSR estimation in lymph nodes, scoring on digital images and the automatic assessment of the TSR using artificial intelligence are described. Conclusion: Digitized scoring of tumor biopsies and resection material offers interesting future perspectives to determine patient prognosis and response to therapy. The fact that the TSR method is relatively easy, quick, and cheap, offers great potential for its implementation in routine diagnostics, but this requires high quality validation studies

Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning

Introduction: The excellent soft-tissue contrast of magnetic resonance imaging (MRI) is appealing for delineation of organs-at-risk (OARs) as it is required for radiation therapy planning (RTP). In the last decade there has been an increasing interest in using deep-learning (DL) techniques to shorten the labor-intensive manual work and increase reproducibility. This paper focuses on the automatic segmentation of 27 head-and-neck and 10 male pelvis OARs with deep-learning methods based on T2-weighted MR images.Method: The proposed method uses 2D U-Nets for localization and 3D U-Net for segmentation of the various structures. The models were trained using public and private datasets and evaluated on private datasets only.Results and discussion: Evaluation with ground-truth contours demonstrated that the proposed method can accurately segment the majority of OARs and indicated similar or superior performance to state-of-the-art models. Furthermore, the auto-contours were visually rated by clinicians using Likert score and on average, 81% of them was found clinically acceptable

Long-term shelf-life liposomes for delivery of prednisolone and budesonide

Liposomes are nanoscale drug delivery systems built up from lipid layers and are able to spontaneously self- assemble in an aqueous environment. Both hydrophilic and hydrophobic drugs can be delivered by liposomes and this kind of nanoformulation offers many advantages regarding biodistribution, drug absorption and controlled drug release. Corticosteroids as lipophilic molecules are able to integrate into the lipid bilayer. This novel approach can improve the efficacy of several anti-inflammatory, such as asthma therapy. Our aim was to create liposomes with long shelf-life, which can incorporate and release corticosteroids such as Prednisolone (Pred) and Budesonide (Bud) at the temperature of inflamed tissues. Two kinds of liposome samples were prepared from three different kinds of phospholipids to get unilamellar vesicles with 100 nm in diameter and characterize their physicochemical properties and effect on living cells. Their main phase transition tem- perature in the physiologically relevant temperature range was measured by differential scanning calorimetry. According to the size distributions determined by dynamic light scattering, all drug-containing liposomes were stable for 6 months. All of the liposome types have a slightly negative zeta potential value. The Fourier-transform infrared spectroscopy revealed no chemical interaction between the drug and lipid molecules. The entrapment efficacy was determined by size-exclusion gel chromatography combined with UV–VIS spectrophotometry and it was very high in both cases (between 70 and 87%). The drug leakage was 35–40% for Pred and 6–8% for Bud in the first 30 min. The effect of liposomal drugs on cell viability was measured on the EBC-1 human lung carcinoma cell line. Neither the free corticosteroids nor their liposomal form were toxic to the cells. The cellular inter- nalization of the liposomes was proved by flow cytometry and confocal microscopy. In summary, these liposomes could be useful in the delivery of corticosteroids (Pred or particularly Bud) in more effective asthma therapy, having fewer side effects due to the nanoformulation

Magnetic Resonance Imaging–Based Delineation of Organs at Risk in the Head and Neck Region

Purpose: The aim of this article is to establish a comprehensive contouring guideline for treatment planning using only magnetic resonance images through an up-to-date set of organs at risk (OARs), recommended organ boundaries, and relevant suggestions for the magnetic resonance imaging (MRI)–based delineation of OARs in the head and neck (H&N) region. Methods and Materials: After a detailed review of the literature, MRI data were collected from the H&N region of healthy volunteers. OARs were delineated in the axial, coronal, and sagittal planes on T2-weighted sequences. Every contour defined was revised by 4 radiation oncologists and subsequently by 2 independent senior experts (H&N radiation oncologist and radiologist). After revision, the final structures were presented to the consortium partners. Results: A definitive consensus was reached after multi-institutional review. On that basis, we provided a detailed anatomic and functional description and specific MRI characteristics of the OARs. Conclusions: In the era of precision radiation therapy, the need for well-built, straightforward contouring guidelines is on the rise. Precise, uniform, delineation-based, automated OAR segmentation on MRI may lead to increased accuracy in terms of organ boundaries and analysis of dose-dependent sequelae for an adequate definition of normal tissue complication probability

Deep-learning-based segmentation of organs-at-risk in the head for MR-assisted radiation therapy planning

Segmentation of organs-at-risk (OAR) in MR images has several clinical applications; including radiation therapy (RT) planning. This paper presents a deep-learning-based method to segment 15 structures in the head region. The proposed method first applies 2D U-Net models to each of the three planes (axial, coronal, sagittal) to roughly segment the structure. Then, the results of the 2D models are combined into a fused prediction to localize the 3D bounding box of the structure. Finally, a 3D U-Net is applied to the volume of the bounding box to determine the precise contour of the structure. The model was trained on a public dataset and evaluated on both public and private datasets that contain T2-weighted MR scans of the head-and-neck region. For all cases the contour of each structure was defined by operators trained by expert clinical delineators. The evaluation demonstrated that various structures can be accurately and efficiently localized and segmented using the presented framework. The contours generated by the proposed method were also qualitatively evaluated. The majority (92%) of the segmented OARs was rated as clinically useful for radiation therapy