Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas

Direct correlation of MR-DWI and histopathology of Wilms' tumours through a patient-specific 3D-printed cutting guide

OBJECTIVES

The International Society of Paediatric Oncology-Renal Tumour Study Group (SIOP-RTSG) discourages invasive procedures to determine the histology of paediatric renal neoplasms at diagnosis. Therefore, the histological subtype of Wilms' tumours (WT) is unknown at the start of neoadjuvant chemotherapy. MR-DWI shows potential value as a non-invasive biomarker through apparent diffusion coefficients (ADCs). This study aimed to describe MR characteristics and ADC values of paediatric renal tumours to differentiate subtypes.

MATERIALS AND METHODS

Children with a renal tumour undergoing surgery within the SIOP-RTSG 2016-UMBRELLA protocol were prospectively included between May 2021 and 2023. In the case of a total nephrectomy, a patient-specific cutting guide based on the neoadjuvant MR was 3D-printed, allowing a correlation between imaging and histopathology. Whole-tumour volumes and ADC values were statistically compared with the Mann-Whitney U-test. Direct correlation on the microscopic slide level was analysed through mixed model analysis.

RESULTS

Fifty-nine lesions of 54 patients (58% male, median age 3.0 years (range 0-17.7 years)) were included. Forty-four lesions involved a WT. Stromal type WT showed the lowest median decrease in volume after neoadjuvant chemotherapy (48.1 cm3, range 561.5-(+)332.7 cm3, p = 0.035). On a microscopic slide level (n = 240 slides) after direct correlation through the cutting guide, stromal areas showed a significantly higher median ADC value compared to epithelial and blastemal foci (p < 0.001). With a cut-off value of 1.195 * 10-3 mm2/s, sensitivity, and specificity were 95.2% (95% confidence interval 87.6-98.4%) and 90.5% (95% confidence interval 68.2-98.3%), respectively.

CONCLUSION

Correlation between histopathology and MR-DWI through a patient-specific 3D-printed cutting guide resulted in significant discrimination of stromal type WT from epithelial and blastemal subtypes.

CLINICAL RELEVANCE STATEMENT

Stromal Wilms' tumours could be discriminated from epithelial- and blastemal lesions based on high apparent diffusion coefficient values and limited decrease in volume after neoadjuvant chemotherapy. This may aid in future decision-making, especially concerning discrimination between low- and high-risk neoplasms.

KEY POINTS

MR-DWI shows potential value as a non-invasive biomarker in paediatric renal tumours. The patient-specific cutting guide leads to a correlation between apparent diffusion coefficient values and Wilms' tumour subtype. Stromal areas could be discriminated from epithelial and blastemal foci in Wilms' tumours based on apparent diffusion coefficient values.

Characteristics of T2* and anisotropy parameters in inguinal and epididymal adipose tissues after cold exposure in mice

White adipose tissue (WAT) in mice undergoes browning in response to cold exposure. Brown and beige adipocytes contain multilocular lipid droplets and abundant iron-containing mitochondria expressing uncoupling protein 1 (UCP-1). Cold exposure-induced browning WAT is accompanied by increased density of blood vessels and sympathetic nerve fibres. A previous study reported a more than threefold increase in sympathetic nerve dendritic tone in inguinal white adipose tissue (iWAT) after cold exposure. Therefore, we hypothesized that water molecule diffusion would be more restricted in brown and beige adipocytes compared to white adipocytes. The characteristics of T2* values and anisotropy parameters by diffusion tensor imaging (DTI) in browning WAT are unclear. The aim of the present study was to investigate the effect of cold exposure on T2* values and anisotropy parameters (fractional anisotropy [FA], apparent diffusion coefficient [ADC], radial diffusivity [RD] and eigenvalues λ1, λ2, λ3) in brown adipose tissue (BAT), iWAT and epididymal white adipose tissue (epiWAT). Furthermore, these parameters were investigated in vivo through additional validation experiments in three control mice. Mice in the cold exposure (CE) group were exposed to a cold environment at 4 °C for 10 days, while these in the control (C) group were maintained at 22 °C throughout the experiment. T2* values, FA, ADC, RD and eigenvalues (λ1, λ2, λ3) were measured in BAT, iWAT and epiWAT using a 9.4T magnetic resonance scanner (Bruker Biospin AG). T2* values of epiWAT in the C group were significantly higher than these of BAT in the C group and iWAT in the CE group. No significant differences were observed between groups for FA, ADC, RD, λ1 and λ2 of iWAT and epiWAT. However, the λ3 values of iWAT and epiWAT in the CE group were significantly higher than these of iWAT, epiWAT and BAT in the C group. Compared to ex vivo measurements, in vivo measurements in control mice showed higher T2* values with reduced intertissue variability while maintaining tissue-specific patterns. These results suggest that T2* values and anisotropy parameters might serve as potential markers for the assessment of adipose tissue plasticity. Further studies are required to investigate their utility as non-invasive indicators of browning WAT.

MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma

Purpose

To identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS).

Materials and methods

In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast in vivo MRI, three-dimensional (3D) multi-contrast high-resolution ex vivo MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant.

Results

Three features correlated with ex vivo apparent diffusion coefficient (ADC), and no features correlated with in vivo ADC. Six features demonstrated significant linear relationships with ex vivo T2*, and fifteen features correlated significantly with in vivo T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics.

Conclusion

Registered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.

The National Cancer Institute's Co-Clinical Quantitative Imaging Research Resources for Precision Medicine in Preclinical and Clinical Settings

Genetically engineered mouse models (GEMMs) and patient-derived xenograft mouse models (PDXs) can recapitulate important biological features of cancer. They are often part of precision medicine studies in a co-clinical setting, in which therapeutic investigations are conducted in patients and in parallel (or sequentially) in cohorts of GEMMs or PDXs. Employing radiology-based quantitative imaging in these studies allows in vivo assessment of disease response in real time, providing an important opportunity to bridge precision medicine from the bench to the bedside. The Co-Clinical Imaging Research Resource Program (CIRP) of the National Cancer Institute focuses on the optimization of quantitative imaging methods to improve co-clinical trials. The CIRP supports 10 different co-clinical trial projects, spanning diverse tumor types, therapeutic interventions, and imaging modalities. Each CIRP project is tasked to deliver a unique web resource to support the cancer community with the necessary methods and tools to conduct co-clinical quantitative imaging studies. This review provides an update of the CIRP web resources, network consensus, technology advances, and a perspective on the future of the CIRP. The presentations in this special issue of Tomography were contributed by the CIRP working groups, teams, and associate members.

Whole-Slide Cytometric Feature Mapping for Distinguishing Tumor Genomic Subtypes in Head and Neck Squamous Cell Carcinoma Whole-Slide Images

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease where, in advanced stages, clinical and pathologic stages do not correlate with outcome. Molecular and genomic biomarkers for HNSCC classification have shown promise for prognostic and therapeutic applications. This study utilized automated image analysis techniques in whole-slide images of HNSCC tumors to identify relationships between cytometric features and genomic phenotypes. Hematoxylin and eosin-stained slides of HNSCC tumors (N = 49) were obtained from The Cancer Imaging Archive, along with accompanying clinical, pathologic, genomic, and proteomic reports. Automated nuclear detection was performed across the entirety of slides, and cytometric feature maps were generated. Forty-one cytometric features were evaluated for associations with tumor grade, tumor stage, tumor subsite, and integrated genomic subtype. Thirty-two features demonstrated significant association with integrated genomic subtype when corrected for multiple comparisons. In particular, the basal subtype was visually distinguishable from the chromosomal instability and immune subtypes based on cytometric feature measurements. No features were significantly associated with tumor grade, stage, or subsite. This study provides preliminary evidence that features derived from tissue pathology slides could provide insights into genomic phenotypes of HNSCC.

Direct correlation of MRI with histopathology in pediatric renal tumors through the use of a patient-specific 3-D-printed cutting guide: a feasibility study

BACKGROUND

Pediatric renal tumors are often heterogeneous lesions with variable regions of distinct histopathology. Direct comparison between in vivo imaging and ex vivo histopathology might be useful for identification of discriminating imaging features.

OBJECTIVE

This feasibility study explored the use of a patient-specific three-dimensional (3D)-printed cutting guide to ensure correct alignment (orientation and slice thickness) between magnetic resonance imaging (MRI) and histopathology.

MATERIALS AND METHODS

Before total nephrectomy, a patient-specific cutting guide based on each patient's preoperative renal MRI was generated and 3-D printed, to enable consistent transverse orientation of the histological specimen slices with MRI slices. This was expected to result in macroscopic slices of 5 mm each. The feasibility of the technique was determined qualitatively, through questionnaires administered to involved experts, and quantitatively, based on structured measurements including overlap calculation using the dice similarity coefficient.

RESULTS

The cutting guide was used in eight Wilms tumor patients receiving a total nephrectomy, after preoperative chemotherapy. The median age at diagnosis was 50 months (range: 4-100 months). The positioning and slicing of the specimens were rated overall as easy and the median macroscopic slice thickness of each specimen ranged from 5 to 6 mm. Tumor consistency strongly influenced the practical application of the cutting guide. Digital correlation of a total of 32 slices resulted in a median dice similarity coefficient of 0.912 (range: 0.530-0.960).

CONCLUSION

We report the feasibility of a patient-specific 3-D-printed MRI-based cutting guide for pediatric renal tumors, allowing improvement of the correlation of MRI and histopathology in future studies.

Automated Nuclear Segmentation in Head and Neck Squamous Cell Carcinoma Pathology Reveals Relationships between Cytometric Features and ESTIMATE Stromal and Immune Scores

The tumor microenvironment (TME) plays an important role in the progression of head and neck squamous cell carcinoma (HNSCC). Currently, pathologic assessment of TME is nonstandardized and subject to observer bias. Genome-wide transcriptomic approaches to understanding the TME, while less subject to bias, are expensive and not currently a part of the standard of care for HNSCC. To identify pathology-based biomarkers that correlate with genomic and transcriptomic signatures of TME in HNSCC, cytometric feature maps were generated in a publicly available data set from a cohort of patients with HNSCC, including whole-slide tissue images and genomic and transcriptomic phenotyping (N = 49). Cytometric feature maps were generated based on whole-slide nuclear detection, using a deep-learning algorithm trained for StarDist nuclear segmentation. Cytometric features in each patient were compared to transcriptomic measurements, including Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) scores and stemness scores. With correction for multiple comparisons, one feature (nuclear circularity) demonstrated a significant linear correlation with ESTIMATE stromal score. Two features (nuclear maximum and minimum diameter) correlated significantly with ESTIMATE immune score. Three features (nuclear solidity, nuclear minimum diameter, and nuclear circularity) correlated significantly with transcriptomic stemness score. This study provides preliminary evidence that observer-independent, automated tissue-slide analysis can provide insights into the HNSCC TME which correlate with genomic and transcriptomic assessments.

DWI Metrics Differentiating Benign Intraductal Papillary Mucinous Neoplasms from Invasive Pancreatic Cancer: A Study in GEM Models

KPC (KrasG12D:Trp53R172H:Pdx1-Cre) and CKS (KrasG12D:Smad4L/L:Ptf1a-Cre) mice are genetically engineered mouse (GEM) models that capture features of human pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasms (IPMN), respectively. We compared these autochthonous tumors using quantitative imaging metrics from diffusion-weighted MRI (DW-MRI) and dynamic contrast enhanced (DCE)-MRI in reference to quantitative histological metrics including cell density, fibrosis, and microvasculature density. Our results revealed distinct DW-MRI metrics between the KPC vs. CKS model (mimicking human PDAC vs. IPMN lesion): the apparent diffusion coefficient (ADC) of CKS tumors is significantly higher than that of KPC, with little overlap (mean ± SD 2.24±0.2 vs. 1.66±0.2, p<10−10) despite intratumor and intertumor variability. Kurtosis index (KI) is also distinctively separated in the two models. DW imaging metrics are consistent with growth pattern, cell density, and the cystic nature of the CKS tumors. Coregistration of ex vivo ADC maps with H&E-stained sections allowed for regional comparison and showed a correlation between local cell density and ADC value. In conclusion, studies in GEM models demonstrate the potential utility of diffusion-weighted MRI metrics for distinguishing pancreatic cancer from benign pancreatic cysts such as IPMN.