Superpixel-based conditional random fields (SuperCRF) : incorporating global and local context for enhanced deep learning in melanoma histopathology

Computational pathology-based cell classification algorithms are revolutionizing the study of the tumor microenvironment and can provide novel predictive/prognosis biomarkers crucial for the delivery of precision oncology. Current algorithms used on hematoxylin and eosin slides are based on individual cell nuclei morphology with limited local context features. Here, we propose a novel multi-resolution hierarchical framework (SuperCRF) inspired by the way pathologists perceive regional tissue architecture to improve cell classification and demonstrate its clinical applications. We develop SuperCRF by training a state-of-art deep learning spatially constrained- convolution neural network (SC-CNN) to detect and classify cells from 105 high-resolution (20×) H&E-stained slides of The Cancer Genome Atlas melanoma dataset and subsequently, a conditional random field (CRF) by combining cellular neighborhood with tumor regional classification from lower resolution images (5, 1.25×) given by a superpixel-based machine learning framework. SuperCRF led to an 11.85% overall improvement in the accuracy of the state-of-art deep learning SC-CNN cell classifier. Consistent with a stroma-mediated immune suppressive microenvironment, SuperCRF demonstrated that (i) a high ratio of lymphocytes to all lymphocytes within the stromal compartment (p = 0.026) and (ii) a high ratio of stromal cells to all cells (p < 0.0001 compared to p = 0.039 for SC-CNN only) are associated with poor survival in patients with melanoma. SuperCRF improves cell classification by introducing global and local context-based information and can be implemented in combination with any single-cell classifier. SuperCRF provides valuable tools to study the tumor microenvironment and identify predictors of survival and response to therapy

Investigating the contribution of collagen to the tumor biomechanical phenotype with noninvasive magnetic resonance elastography

Increased stiffness in the extracellular matrix (ECM) contributes to tumor progression and metastasis. Therefore, stromal modulating therapies and accompanying biomarkers are being developed to target ECM stiffness. Magnetic resonance (MR) elastography can noninvasively and quantitatively map the viscoelastic properties of tumors in vivo and thus has clear clinical applications. Herein, we used MR elastography, coupled with computational histopathology, to interrogate the contribution of collagen to the tumor biomechanical phenotype and to evaluate its sensitivity to collagenase-induced stromal modulation. Elasticity (G d) and viscosity (G l) were significantly greater for orthotopic BT-474 (G d = 5.9 ± 0.2 kPa, G l = 4.7 ± 0.2 kPa, n = 7) and luc-MDA-MB-231-LM2-4 (G d = 7.9 ± 0.4 kPa, G l = 6.0 ± 0.2 kPa, n = 6) breast cancer xenografts, and luc-PANC1 (G d = 6.9 ± 0.3 kPa, G l = 6.2 ± 0.2 kPa, n = 7) pancreatic cancer xenografts, compared with tumors associated with the nervous system, including GTML/Trp53KI/KI medulloblastoma (G d = 3.5 ± 0.2 kPa, G l = 2.3 ± 0.2 kPa, n = 7), orthotopic luc-D-212-MG (G d = 3.5 ± 0.2 kPa, G l = 2.3 ± 0.2 kPa, n = 7), luc-RG2 (G d = 3.5 ± 0.2 kPa, G l = 2.3 ± 0.2 kPa, n = 5), and luc-U-87-MG (G d = 3.5 ± 0.2 kPa, G l = 2.3 ± 0.2 kPa, n = 8) glioblastoma xenografts, intracranially propagated luc-MDA-MB-231-LM2-4 (G d = 3.7 ± 0.2 kPa, G l = 2.2 ± 0.1 kPa, n = 7) breast cancer xenografts, and Th-MYCN neuroblastomas (G d = 3.5 ± 0.2 kPa, G l = 2.3 ± 0.2 kPa, n = 5). Positive correlations between both elasticity (r = 0.72, P r = 0.78, P G d (P = 0.002) and G l (P = 0.0006) in orthotopic breast tumors. Texture analysis of extracted images of picrosirius red staining revealed significant negative correlations of entropy with G d (r = -0.69, P G l (r = -0.76, P G d (r = 0.75, P G l (r = 0.78, P < 0.0001). MR elastography can thus provide sensitive imaging biomarkers of tumor collagen deposition and its therapeutic modulation. SIGNIFICANCE: MR elastography enables noninvasive detection of tumor stiffness and will aid in the development of ECM-targeting therapies

Investigating the contribution of hyaluronan to the breast tumour microenvironment using multiparametric MRI and MR elastography

Hyaluronan (HA) is a key component of the dense extracellular matrix in breast cancer, and its accumulation is associated with poor prognosis and metastasis. Pegvorhyaluronidase alfa (PEGPH20) enzymatically degrades HA and can enhance drug delivery and treatment response in preclinical tumour models. Clinical development of stromal‐targeted therapies would be accelerated by imaging biomarkers that inform on therapeutic efficacy in vivo. Here, PEGPH20 response was assessed by multiparametric magnetic resonance imaging (MRI) in three orthotopic breast tumour models. Treatment of 4T1/HAS3 tumours, the model with the highest HA accumulation, reduced T1 and T2 relaxation times and the apparent diffusion coefficient (ADC), and increased the magnetisation transfer ratio, consistent with lower tissue water content and collapse of the extracellular space. The transverse relaxation rate R2* increased, consistent with greater erythrocyte accessibility following vascular decompression. Treatment of MDA‐MB‐231 LM2‐4 tumours reduced ADC and dramatically increased tumour viscoelasticity measured by MR elastography. Correlation matrix analyses of data from all models identified ADC as having the strongest correlation with HA accumulation, suggesting that ADC is the most sensitive imaging biomarker of tumour response to PEGPH20

Non-Invasive Prostate Cancer Characterization with Diffusion-Weighted MRI: Insight from In silico Studies of a Transgenic Mouse Model

Diffusion-weighted magnetic resonance imaging (DWI) enables non-invasive, quantitative staging of prostate cancer via measurement of the apparent diffusion coefficient (ADC) of water within tissues. In cancer, more advanced disease is often characterized by higher cellular density (cellularity), which is generally accepted to correspond to a lower measured ADC. A quantitative relationship between tissue structure and in vivo measurements of ADC has yet to be determined for prostate cancer. In this study, we establish a theoretical framework for relating ADC measurements with tissue cellularity and the proportion of space occupied by prostate lumina, both of which are estimated through automatic image processing of whole-slide digital histology samples taken from a cohort of six healthy mice and nine transgenic adenocarcinoma of the mouse prostate (TRAMP) mice. We demonstrate that a significant inverse relationship exists between ADC and tissue cellularity that is well characterized by our model, and that a decrease of the luminal space within the prostate is associated with a decrease in ADC and more aggressive tumor subtype. The parameters estimated from our model in this mouse cohort predict the diffusion coefficient of water within the prostate-tissue to be 2.18 × 10−3 mm2/s (95% CI: 1.90, 2.55). This value is significantly lower than the diffusion coefficient of free water at body temperature suggesting that the presence of organelles and macromolecules within tissues can drastically hinder the random motion of water molecules within prostate tissue. We validate the assumptions made by our model using novel in silico analysis of whole-slide histology to provide the simulated ADC (sADC); this is demonstrated to have a significant positive correlation with in vivo measured ADC (r2 = 0.55) in our mouse population. The estimation of the structural properties of prostate tissue is vital for predicting and staging cancer aggressiveness, but prostate tissue biopsies are painful, invasive, and are prone to complications such as sepsis. The developments made in this study provide the possibility of estimating the structural properties of prostate tissue via non-invasive virtual biopsies from MRI, minimizing the need for multiple tissue biopsies and allowing sequential measurements to be made for prostate cancer monitoring