Tumor Quantification Research Articles

MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples.

DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. Here we present MIMESIS, a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7200 samples demonstrate the capability of MIMESIS to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To assess our framework for clinical applications, we designed a MIMESIS-informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients' circulation. This study provides evidence that our extremely parsimonious approach can be used to develop cost-effective and highly scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice.

AI-Driven Robust Kidney and Renal Mass Segmentation and Classification on 3D CT Images.

Early intervention in kidney cancer helps to improve survival rates. Abdominal computed tomography (CT) is often used to diagnose renal masses. In clinical practice, the manual segmentation and quantification of organs and tumors are expensive and time-consuming. Artificial intelligence (AI) has shown a significant advantage in assisting cancer diagnosis. To reduce the workload of manual segmentation and avoid unnecessary biopsies or surgeries, in this paper, we propose a novel end-to-end AI-driven automatic kidney and renal mass diagnosis framework to identify the abnormal areas of the kidney and diagnose the histological subtypes of renal cell carcinoma (RCC). The proposed framework first segments the kidney and renal mass regions by a 3D deep learning architecture (Res-UNet), followed by a dual-path classification network utilizing local and global features for the subtype prediction of the most common RCCs: clear cell, chromophobe, oncocytoma, papillary, and other RCC subtypes. To improve the robustness of the proposed framework on the dataset collected from various institutions, a weakly supervised learning schema is proposed to leverage the domain gap between various vendors via very few CT slice annotations. Our proposed diagnosis system can accurately segment the kidney and renal mass regions and predict tumor subtypes, outperforming existing methods on the KiTs19 dataset. Furthermore, cross-dataset validation results demonstrate the robustness of datasets collected from different institutions trained via the weakly supervised learning schema.

Evaluation of a respiratory motion-corrected image reconstruction algorithm in 2-[18F]FDG and [68Ga]Ga-DOTA-NOC PET/CT: impacts on image quality and tumor quantification

Respiratory motions may cause artifacts on positron emission tomography (PET) images that degrade image quality and quantification accuracy. This study aimed to evaluate the effect of a respiratory motion-corrected image reconstruction (MCIR) algorithm on image quality and tumor quantification compared with nongated/nonmotion-corrected reconstruction. We used a phantom consisting of 5 motion spheres immersed in a chamber driven by a motor. The spheres and the background chamber were filled with 18F solution at a sphere-to-background ratio of 5:1. We enrolled 42 and 16 patients undergoing 2-deoxy-2-[18F]fluoro-D-glucose {2-[18F]FDG} and 68Ga-labeled [1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid]-1-Nal3-octreotide {[68Ga]Ga-DOTA-NOC} PET/computed tomography (CT) from whom 74 and 30 lesions were segmented, respectively. Three reconstructions were performed: data-driven gating-based motion correction (DDGMC), external vital signal module-based motion correction (VSMMC), and noncorrection reconstruction. The standardized uptake values (SUVs) and the volume of the spheres and the lesions were measured and compared among the 3 reconstruction groups. The image noise in the liver was measured, and the visual image quality of motion artifacts was scored by radiologists in the patient study. In the phantom study, the spheres' SUVs increased by 26-36%, and the volumes decreased by 35-38% in DDGMC and VSMMC compared with the noncorrection group. In the 2-[18F]FDG PET patient study, the lesions' SUVs had a median increase of 10.87-12.65% while the volumes had a median decrease of 14.88-15.18% in DDGMC and VSMMC compared with those of noncorrection. In the [68Ga]Ga-DOTA-NOC PET patient study, the lesions' SUVs increased by 14.23-15.45%, and the volumes decreased by 19.11-20.94% in DDGMC and VSMMC. The image noise in the liver was equal between the DDGMC, VSMMC, and noncorrection groups. Radiologists found improved image quality in more than 45% of the cases in DDGMC and VSMMC compared with the noncorrection group. There was no statistically significant difference in SUVs, volumes, or visual image quality scores between DDGMC and VSMMC. MCIR improves tumor quantification accuracy and visual image quality by reducing respiratory motion artifacts without compromised image noise performance or elongated acquisition time in 2-[18F]FDG and [68Ga]Ga-DOTA-NOC PET/CT tumor imaging. The performance of DDG-driven MCIR is as good as that of the external device-driven solution.

Uncoupling protein 2 deficiency of non-cancerous tissues inhibits the progression of pancreatic cancer in mice

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a disease of the elderly mostly because its development from preneoplastic lesions depends on the accumulation of gene mutations and epigenetic alterations over time. How aging of non-cancerous tissues of the host affects tumor progression, however, remains largely unknown. MethodsWe took advantage of a model of accelerated aging, uncoupling protein 2-deficient (Ucp2 knockout, Ucp2 KO) mice, to investigate the growth of orthotopically transplanted Ucp2 wild-type (WT) PDAC cells (cell lines Panc02 and 6606PDA) in vivo and to study strain-dependent differences of the PDAC microenvironment. ResultsMeasurements of tumor weights and quantification of proliferating cells indicated a significant growth advantage of Panc02 and 6606PDA cells in WT mice compared to Ucp2 KO mice. In tumors in the knockout strain, higher levels of interferon-γ mRNA despite similar numbers of tumor-infiltrating T cells were observed. 6606PDA cells triggered a stronger stromal reaction in Ucp2 KO mice than in WT animals. Accordingly, pancreatic stellate cells from Ucp2 KO mice proliferated at a higher rate than cells of the WT strain when they were incubated with conditioned media from PDAC cells. ConclusionsUcp2 modulates PDAC microenvironment in a way that favors tumor progression and implicates an altered stromal response as one of the underlying mechanisms.

ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation.

Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape, and location are important for further tumor quantification and classification. However, segmenting small tumors in ultrasound images is challenging due to the speckle noise, varying tumor shapes and sizes among patients, and the existence of tumor-like image regions. Recently, deep learning-based approaches have achieved great success in biomedical image analysis, but current state-of-the-art approaches achieve poor performance for segmenting small breast tumors. In this paper, we propose a novel deep neural network architecture, namely the Enhanced Small Tumor-Aware Network (ESTAN), to accurately and robustly segment breast tumors. The Enhanced Small Tumor-Aware Network introduces two encoders to extract and fuse image context information at different scales, and utilizes row-column-wise kernels to adapt to the breast anatomy. We compare ESTAN and nine state-of-the-art approaches using seven quantitative metrics on three public breast ultrasound datasets, i.e., BUSIS, Dataset B, and BUSI. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on small tumor segmentation. Specifically, the Dice similarity coefficient (DSC) of ESTAN on the three datasets is 0.92, 0.82, and 0.78, respectively; and the DSC of ESTAN on the three datasets of small tumors is 0.89, 0.80, and 0.81, respectively.

Signal Amplification Pretargeted PET/Fluorescence Imaging Based on Human Serum Albumin-Encapsulated GdF3 Nanoparticles for Diagnosis of Ovarian Cancer.

Different modal imaging techniques could be complementary in tumor diagnosis. Human serum albumin (HSA)-encapsulated GdF3 nanoparticles were developed as T1 magnetic resonance imaging (MRI) contrast agents. However, no significant T1 enhancement in the tumor site of the SKOV3 human ovarian cancer xenograft tumor model was observed within 3 h after injection of tetrazine-modified GdF3@HSA NPs through small-animal MRI. After intravenous injection of 18F (or Cy7)-labeled Reppe anhydride, pretargeted positron emission tomography (PET) (near-infrared (NIR) fluorescence) imaging was used to reveal the pharmacokinetics of GdF3@HSA NPs in the SKOV3 xenograft mouse model to locate the tumor. The probe based on Reppe anhydride achieved rapid ligation with tetrazine-modified GdF3@HSA nanoparticles (NPs), which accumulated in tumor through Reppe anhydride/tetrazine bioorthogonal chemistry. This pretargeting strategy enabled excellent tumor visualization and quantification at an early period after nanoparticle injection (3 h p.i.), while the MRI images with significant T1 enhancement could be obtained until 24 h after injection of Gd-based contrast agents only. In vivo pretargeted multimodal imaging based on the tetrazine/Reppe anhydride system using HSA-encapsulated GdF3 nanoparticles would be beneficial for amplification of the imaging signal in the disease site and enhancing diagnostic efficiency.

Ultrasound Molecular Imaging and Its Applications in Cancer Diagnosis and Therapy.

Ultrasound imaging is regarded as a highly sensitive imaging modality used in routine clinical examinations. Over the last several decades, ultrasound contrast agents have been widely applied in ultrasound molecular cancer imaging to improve the detection, characterization, and quantification of tumors. To date, a few new potential preclinical and clinical applications regarding ultrasound molecular cancer imaging are being investigated. This review presents an overview of the various kinds of ultrasound contrast agents employed in ultrasound molecular imaging and advanced imaging techniques using these contrast agents. Additionally, we discuss the recent enormous development of ultrasound contrast agents in the relevant preclinical and clinical applications, highlight the recent challenges which need to be overcome to accelerate the clinical translation, and discuss the future perspective of ultrasound molecular cancer imaging using various contrast agents. As a highly promising and valuable tumor-specific imaging technique, it is believed that ultrasound molecular imaging will pave an accurate and efficient way for cancer diagnosis.

Reliability of gradient-based segmentation for measuring metabolic parameters influenced by uptake time on 18F-PSMA-1007 PET/CT for prostate cancer.

PurposeTo determine an optimal setting for functional contouring and quantification of prostate cancer lesions with minimal variation by evaluating metabolic parameters on 18F-PSMA-1007 PET/CT measured by threshold-based and gradient-based methods under the influence of varying uptake time.Methods and materialsDual time point PET/CT was chosen to mimic varying uptake time in clinical setting. Positive lesions of patients who presented with newly diagnosed disease or biochemical recurrence after total prostatectomy were reviewed retrospectively. Gradient-based and threshold-based tools at 40%, 50% and 60% of lesion SUVmax (MIM 6.9) were used to create contours on PET. Contouring was considered completed if the target lesion, with its hottest voxel, was delineated from background tissues and nearby lesions under criteria specific to their operations. The changes in functional tumour volume (FTV) and metabolic tumour burden (MTB, defined as the product of SUVmean and FTV) were analysed. Lesion uptake patterns (increase/decrease/stable) were determined by the percentage change in tumour SUVmax at ±10% limit.ResultsA total of 275 lesions (135 intra-prostatic lesions, 65 lymph nodes, 45 bone lesions and 30 soft tissue lesions in pelvic region) in 68 patients were included. Mean uptake time of early and delayed imaging were 94 and 144 minutes respectively. Threshold-based method using 40% to 60% delineated only 85 (31%), 110 (40%) and 137 (50%) of lesions which all were contoured by gradient-based method. Although the overall percentage change using threshold at 50% was the smallest among other threshold levels in FTV measurement, it was still larger than gradient-based method (median: 50%=-7.6% vs gradient=0%). The overall percentage increase in MTB of gradient-based method (median: 6.3%) was compatible with the increase in tumour SUVmax. Only a small proportion of intra-prostatic lesions (<2%), LN (<4%), bone lesions (0%) and soft tissue lesions (<4%) demonstrated decrease uptake patterns.ConclusionsWith a high completion rate, gradient-based method is reliable for prostate cancer lesion contouring on 18F-PSMA-1007 PET/CT. Under the influence of varying uptake time, it has smaller variation than threshold-based method for measuring volumetric parameters. Therefore, gradient-based method is recommended for tumour delineation and quantification on 18F-PSMA-1007 PET/CT.

Quantification of Tumor and Angiogenesis-Related Markers in Ovarian Cancer Models by a Digital Pathology Approach.

Digital pathology has the potential to quantify tumor markers accurately and reproducibly with various cellular and subcellular localizations in tissues, thus filling a need in cancer research. As a case study, we quantified the percentage of necrosis, microvessels density, and monocarboxylate transporter 4 (MCT4) expression in two ovarian cancer patient-derived xenograft (PDX) models subcutaneously injected in NOD/SCID mice. PDX models were treated with bevacizumab, an antiangiogenic drug, that targets vascular endothelial growth factor A (VEGF-A). Specific signal analysis algorithms allowed us to study morphologic, vascular, and metabolic modifications induced by antiangiogenic therapy by a quantitative, reproducible, and reliable approach.

CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction.

To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction. A CEST-MRF pulse sequence originally designed for animal imaging was modified to conform to hardware limits on clinical scanners while keeping scan time under 2min. Quantitative MRF reconstruction was performed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the six parameter DRONE reconstruction was tested in simulations using a digital brain phantom. A healthy subject was scanned with the CEST-MRF sequence, conventional MRF and CEST sequences for comparison. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter and gray matter. The clinical utility of CEST-MRF was demonstrated on four patients with brain metastases in comparison to standard clinical imaging sequences. Tumors were segmented into edema, solid core, and necrotic core regions and the CEST-MRF values compared to the contra-lateral side. DRONE reconstruction of the digital phantom yielded a normalized RMS error of ≤7% for all parameters. The CEST-MRF parameters were in good agreement with those from conventional MRF and CEST sequences and previous studies. The mean concordance correlation coefficient for all six parameters was 0.98 ± 0.01 in white matter and 0.98 ± 0.02 in gray matter. The CEST-MRF values in nearly all tumor regions were significantly different (P=0.05) from each other and the contra-lateral side. Combination of EPI readout and deep learning reconstruction enabled fast, accurate and reproducible CEST-MRF in brain tumors.

JS07.5.A 18F-fluciclovine PET and multi-parametric MRI to distinguish pseudoprogression from tumor progression in post-treatment glioblastoma

Abstract Background Differentiation of tumor progression (TP) from pseudoprogression (PsP) is a major unmet need in glioblastoma (GBM). 18F-Fluciclovine is a synthetic amino acid PET radiotracer with higher uptake in tumor tissue vs. areas of treatment-related change. We aimed to assess the combined value of 18F-Fluciclovine PET and multi-parametric MRI for differentiating PsP from TP. Material and Methods We enrolled 30 patients with GBM with a new or enlarging contrast-enhancing lesion on MRI after chemoradiotherapy who were planned for surgical resection of the lesion. Patients underwent pre-operative 18F-Fluciclovine PET and multi-parametric MRI. Following surgery, the relative percentages of viable tumor and therapy-related changes observed on histopathology were quantified. Patients were categorized as TP if viable tumor represented ≥ 50% of the specimen, mixed TP if &amp;lt; 50% and &amp;gt; 10%, and PsP if ≤ 10%. SUVmax, SUVpeak, and 50% threshold SUVmean were calculated and normalized to contralateral brain, pituitary gland, and superior sagittal sinus (SSS). The least absolute shrinkage and selection operator (LASSO) was used to determine the variables most predictive of tumor percentage. The strength of association between the primary outcome and selected variables was assessed by Pearson’s or Point-biserial correlation. Results 18 patients with TP, 4 with mixed TP-PsP, and 8 with PsP were included. There was a positive correlation between 50% threshold SUV mean measured from PET images acquired 50-60 minutes post-injection and rCBVmax by MRI and tumor percentage by histology (r= 0.56; p= 0.004 and r=0.50; p=0.012 respectively). 40-50 minutes SUVmax (OR=1.78 rpb=0.51) and rCBVmax (OR=1.64, rpb=0.48) were positively correlated with tumor TP/mixed TP group. Patients who demonstrated TP/mixed TP-PsP had significantly higher 40-50 minutes SUVmax compared to patients with histological PsP (6.71±2.03 vs 3.93±1.63; p=0.012). 40-50 minutes SUVmax cut-off of 4.46 provided 90% sensitivity and 80% specificity for differentiation of TP/mixed TP-PsP from PsP (AUC=0.88). Combining a 40-50 minutes SUVmax cut-off of 4.46 and an rCBVmax cut-off of 3.67 provided 100% sensitivity and 100% specificity for differentiating TP/mixed TP-PsP from PsP (AUC=1). Patients who demonstrated TP had a significantly higher 40-50 minutes SUVmax compared to patients with histological PsP (6.99±2.06 vs 3.93±1.63; p=0.008). A 40-50 minutes SUVmax cut-off of 4.66 provided 94% sensitivity and 80% specificity for differentiation of TP from PsP (AUC=0.89). Conclusion 18F-Fluciclovine PET uptake is positively correlated with viable tumor quantification on histology and can accurately differentiate PsP from TP in patients with GBM. Further independent studies are required to cross-validate these promising early results.

Abstract PO025: GM-CSF neutralization enhances the efficacy of chemotherapy in an orthotopic murine model of cholangiocarcinoma

Abstract Introduction: With current effective treatments in cholangiocarcinoma (CCA) lacking, we expounded on our prior work highlighting the critical role of the granulocyte-macrophage-colony stimulating factor ligand/receptor (GM-CSF/GM-CSFRα) signaling pathway by evaluating GM-CSF neutralization combined with first-line chemotherapy in a novel murine model of CCA. Methods: Resected CCA specimens were used to construct a human tissue macro-array. Immunohistochemistry (IHC) staining for GM-CSFRα and digital quantification of staining intensity was performed. A novel syngeneic CCA cell line derived from spontaneous murine CCA from LSL-KrasG12D; Tp53Flox/Flox −; Alb-Cre (KPPC) mice was surgically injected into the left hepatic lobes of C57BL/6J mice. Disease onset and extent of progression was monitored over time using ultrasonography (US) in groups of mice enrolled into treatment with vehicle, gemcitabine, anti-GM-CSF, and gemcitabine plus anti-GM-CSF. Mice were sacrificed at predetermined endpoints for flow cytometry analysis. Results: IHC analysis and digital quantification of resected human tumors demonstrated that CCA expresses significantly higher levels of GM-CSFRα (p&amp;lt;0.01) compared to uninvolved normal liver. Mice injected with a novel syngeneic CCA cell line expressing high levels of GM-CSF protein uniformly developed tumors fourteen days after orthotopic implantation. Histopathological evaluation of orthotopic CCA tumors demonstrated faithful recapitulation of both spontaneous murine and human disease with marked development of malignant biliary ductal epithelium (CK7), desmoplastic stroma (Sirius red, trichrome), and prominent inflammatory leukocytic infiltrate (CD45). Flow cytometry analysis of end-stage orthotopically implanted tumors demonstrated a progressive expansion of myeloid cells (70.7% CD11b+) compared to CD8+ (0.8%) and CD4+ (2.0%) T cells, indicative of a highly immunosuppressive tumor immune microenvironment. Combination therapy with GM-CSF neutralization and chemotherapy significantly reduced tumor size as screened via US compared to vehicle (p&amp;lt;0.0001), anti-GM-CSF (p&amp;lt;0.01), and chemotherapy alone (p&amp;lt;0.01). In addition, combination therapy reduced the rate of tumor growth compared to each of the three other treatment groups (all p&amp;lt;0.0001). Finally, tumor weights were significantly reduced in the combination therapy group versus the vehicle (p&amp;lt;0.0001), anti-GM-CSF (p&amp;lt;0.001), and chemotherapy alone (p&amp;lt;0.05) cohorts. Conclusion: Human CCA demonstrates elevated levels of GM-CSFRα compared to normal liver. Neutralization of GM-CSF in combination with chemotherapy reduced tumor growth and weight in a novel murine model of CCA. Targeting the GM-CSF/GM-CSFRα pathway represents an innovative means of improving tumor control in CCA. Citation Format: Yatee A Dave, Paul R Burchard, Nicholas A Ullman, Mary Georger, Luis I Ruffolo, Brian A Belt, David C Linehan. GM-CSF neutralization enhances the efficacy of chemotherapy in an orthotopic murine model of cholangiocarcinoma [abstract]. In: Proceedings of the AACR Special Conference: Advances in the Pathogenesis and Molecular Therapies of Liver Cancer; 2022 May 5-8; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(17_Suppl):Abstract nr PO025.

604P T cell density and immune phenotypes at the invasive margin correlate with prognosis in epithelial vulvar cancer

Quantification of tumor infiltrating lymphocytes (TILs) in the cancer microenvironment has become of increasing interest in immuno-oncology. Only little is known about TILs infiltration and its predictive value in vulvar cancer.

Non-invasive PET/MR Imaging in an Orthotopic Mouse Model of Hepatocellular Carcinoma.

Preclinical experimental models of hepatocellular carcinoma (HCC) that recapitulate human disease represent an important tool to study tumorigenesis and evaluate novel therapeutic approaches. Non-invasive whole-body imaging using positron emission tomography (PET) provides critical insights into the in vivo characteristics of tissues at the molecular level in real-time. We present here a protocol for orthotopic HCC xenograft creation with and without hepatic artery ligation (HAL) to induce tumor hypoxia and the assessment of their tumor metabolism in vivo using [18F]Fluoromisonidazole ([18F]FMISO) and [18F]Fluorodeoxyglucose ([18F]FDG) PET/magnetic resonance (MR) imaging. Tumor hypoxia could be readily visualized using the hypoxia marker [18F]FMISO, and it was found that the [18F]FMISO uptake was higher in HCC mice that underwent HAL than in the non-HAL group, whereas [18F]FDG could not distinguish tumor hypoxia between the two groups. HAL tumors also displayed a higher level of hypoxia-inducible factor (HIF)-1α expression in response to hypoxia. Quantification of HAL tumors showed a 2.3-fold increase in [18F]FMISO uptake based on the standardized value uptake (SUV) approach.

T-Cell Density at the Invasive Margin and Immune Phenotypes Predict Outcome in Vulvar Squamous Cell Cancer.

Simple SummaryQuantification of tumor infiltrating lymphocytes (TILs) in the cancer microenvironment has become of increasing interest in immuno-oncology and has been therefore studied extensively in all “high prevalence” cancer entities. However, only little is known about TILs infiltration in rare cancer entities, such as vulvar cancer. Here, we have studied an exceptional large multicenter cohort of 530 vulvar squamous cell cancer specimens using immunohistochemistry. The study was conducted using large tissue section to quantitate the CD3+/CD8+ T-cell density along the invasive margin compared to the center of the tumor. The CD3+ T-cell density at the invasive margin showed the strongest prognostic value. Moreover, analysing the interplay between both T-cell densities—at different locations—enabled us to identify three major immune phenotypes that showed strong prognostic relevance. Accordingly, the sole analysis of CD3+ T-cells at the invasive margin—rather than a cumbersome immune score—predicts patient’s outcome in vulvar cancer.Background: Although quantification of tumor infiltrating lymphocytes (TILs) has become of increasing interest in immuno-oncology, only little is known about TILs infiltration in the tumor microenvironment and its predictive value in vulvar cancer. Methods: Immunohistochemistry and automated digital image analysis was applied to measure the densities of CD3+ (DAKO, #IR503) and CD8+ (DAKO, #IR623) TILs at the invasive margin and in the center of 530 vulvar squamous cell cancers. Results: An elevated density of CD3+ T-cell at the invasive margin was significantly associated with low tumor stage (p = 0.0012) and prolonged survival (overall survival [OS] p = 0.0027, progression free survival [PFS] p = 0.024) and was independent from tumor stage, nodal stage, grade, and HPV-status in multivariate analysis (p < 0.05). The prognostic impact of CD3+ cells in the center of the tumor was weaker compared to the invasive margin (OS p = 0.046, PFS p = 0.031) and lacking for CD8+ T-cell densities at any location (p ≥ 0.14 each). Unsupervised clustering of CD3+ and CD8+ T-cell densities identified three major subgroups corresponding to the immune desert (137 patients), immune excluded (220 patients) and immune inflamed phenotypes (133 patients). Survival analysis revealed a particular poor prognosis for the immune desert phenotype for OS (p = 0.0071) and PFS (p = 0.0027). Conclusion: Our data demonstrate a high prognostic value of CD3+ T-cells at the invasive margin and immune phenotypes in vulvar squamous cell cancer.

Impact of low injected activity on data driven respiratory gating for PET/CT imaging with continuous bed motion.

Data driven respiratory gating (DDG) in positron emission tomography (PET) imaging extracts respiratory waveforms from the acquired PET data obviating the need for dedicated external devices. DDG performance, however, degrades with decreasing detected number of coincidence counts. In this paper, we assess the clinical impact of reducing injected activity on a new DDG algorithm designed for PET data acquired with continuous bed motion (CBM_DDG) by evaluating CBM_DDG waveforms, tumor quantification, and physician's perception of motion blur in resultant images. Forty patients were imaged on a Siemens mCT scanner in CBM mode. Reduced injected activity was simulated by generating list mode datasets with 50% and 25% of the original data (100%). CBM_DDG waveforms were compared to that of the original data over the range between the aortic arch and the center of the right kidney using the Pearson correlation coefficient (PCC). Tumor quantification was assessed by comparing the maximum standardized uptake value (SUVmax) and peak SUV (SUVpeak) of reconstructed images from the various list mode datasets using elastic motion deblurring (EMDB) reconstruction. Perceived motion blur was assessed by three radiologists of one lesion per patient on a continuous scale from no motion blur (0) to significant motion blur (3). The mean PCC of the 50% and 25% dataset waveforms was 0.74 ± 0.18 and 0.59 ± 0.25, respectively. In comparison to the 100% datasets, the mean SUVmax increased by 2.25% (p = 0.11) for the 50% datasets and by 3.91% (p = 0.16) for the 25% datasets, while SUVpeak changes were within ±0.25%. Radiologist evaluations of motion blur showed negligible changes with average values of 0.21, 0.3, and 0.28 for the 100%, 50%, and 25% datasets. Decreased injected activities degrades the resultant CBM_DDG respiratory waveforms; however this decrease has minimal impact on quantification and perceived image motion blur.

Quantification and visualization of metastatic lung tumors in mice.

Histopathological examination is important for the diagnosis of various diseases. Conventional histopathology provides a two-dimensional view of the tissues, and requires the tissue to be extracted, fixed, and processed using histotechnology techniques. However, there is an increasing need for three-dimensional (3D) images of structures in biomedical research. The objective of this study was to develop reliable, objective tools for visualizing and quantifying metastatic tumors in mouse lung using micro-computed tomography (micro-CT), optical coherence tomography (OCT), and field emission-scanning electron microscopy (FE-SEM). Melanoma cells were intravenously injected into the tail vein of 8-week-old C57BL/6 mice. The mice were euthanized at 2 or 4weeks after injection. Lungs were fixed and examined by micro-CT, OCT, FE-SEM, and histopathological observation. Micro-CT clearly distinguished between tumor and normal cells in surface and deep lesions, thereby allowing 3D quantification of the tumor volume. OCT showed a clear difference between the tumor and surrounding normal tissues. FE-SEM clearly showed round tumor cells, mainly located in the alveolar wall and growing inside the alveoli. Therefore, whole-tumor 3D imaging successfully visualized the metastatic tumor and quantified its volume. This promising approach will allow for fast and label-free 3D phenotyping of diverse tissue structures.

Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning

Objective. In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value (SUV). For small objects, the accuracy of SUV estimates is limited by the noise properties of PET images and the partial volume effect. There is need for methods that provide more accurate and reproducible quantification of radiotracer uptake. Approach. In this work, we present a deep learning approach with the aim of improving quantification of lung tumour radiotracer uptake and tumour shape definition. A set of simulated tumours, assigned with ‘ground truth’ radiotracer distributions, are used to generate realistic PET raw data which are then reconstructed into PET images. In this work, the ground truth images are generated by placing simulated tumours characterised by different sizes and activity distributions in the left lung of an anthropomorphic phantom. These images are then used as input to an analytical simulator to simulate realistic raw PET data. The PET images reconstructed from the simulated raw data and the corresponding ground truth images are used to train a 3D convolutional neural network. Results. When tested on an unseen set of reconstructed PET phantom images, the network yields improved estimates of the corresponding ground truth. The same network is then applied to reconstructed PET data generated with different point spread functions. Overall the network is able to recover better defined tumour shapes and improved estimates of tumour maximum and median activities. Significance. Our results suggest that the proposed approach, trained on data simulated with one scanner geometry, has the potential to restore PET data acquired with different scanners.

Cycle-consistent learning-based hybrid iterative reconstruction for whole-body PET imaging

Objective. To develop a cycle-consistent learning-based hybrid iterative reconstruction (IR) method that takes only slightly longer than analytic reconstruction, while pursuing the image resolution and tumor quantification achievable by IR for whole-body PET imaging. Approach. We backproject the raw positron emission tomography (PET) data to generate a blurred activity distribution. From the backprojection to the IR label, a reconstruction mapping that approximates the deblurring filters for the point spread function and the physical effects of the PET system is unrolled to a neural network with stacked convolutional layers. By minimizing the cycle-consistent loss, we train the reconstruction and inverse mappings simultaneously. Main results. In phantom study, the proposed method results in an absolute relative error (RE) of the mean activity of 4.0% ± 0.7% in the largest hot sphere, similar to the RE of the full-count IR and significantly smaller than that obtained by CycleGAN postprocessing. Achieving a noise reduction of 48.1% ± 0.5% relative to the low-count IR, the proposed method demonstrates advantages over the low-count IR and CycleGAN in terms of resolution maintenance, contrast recovery, and noise reduction. In patient study, the proposed method obtains a noise reduction of 44.6% ± 8.0% for the lung and the liver, while maintaining the regional mean activity in both simulated lesions and real tumors. The run time of the proposed method is only half that of the conventional IR. Significance. The proposed cycle-consistent learning from the backprojection rather than the raw PET data or an IR result enables improved reconstruction accuracy, reduced memory requirements, and fast implementation speeds for clinical whole-body PET imaging.

Accuracy of whole slide image based image analysis is adversely affected by preanalytical factors such as stained tissue slide and paraffin block age

BackgroundPersonalized medicine and accurate quantification of tumor and biomarker expression have become the cornerstone of cancer diagnostics. This requires Quality Control (QC) of research tissue samples to confirm adequate targeted tumor tissue sampling. Digitalization of stained tissue slides offer a precious way to archive, preserve, and retrieve necessary information when needed. This study is aimed to assess the most significant pre-analytic and analytic factors that might contribute to the efficacy of obtaining accurate whole slide images (WSIs) interpretation. Various studies are needed to identifysuch factors to allow for appropriate AI application and adequate tumor area/percentage quantification. MethodsHematoxylene and Eosine (H&E) satined WSIs collected from tissue specimens provided by the Cooperative Human Tissue Network (CHTN) Midwestern Division (CHTNMWD) were analyzed. Tissue specimens were processed, fixed, stained, and scanned contemporaneously (within 1 month). Two cohorts of malignant, colorectal cancer, 20X WSI (ScanscopeXT, Leica Biosystems, Illinois), were assembled. The study identified a "recent cohort" that included 76 WSIs created on 2018 or later. "Aged cohort" included 73 WSIs from specimens procured in the period of (2012-2014). Twenty recent WSIs of adenocarcinoma cases were used to construct WSIs analysis algorithms (VIS, Visiopharm A/S, Denmark) using machine learning to produce morphometric maps and calculate tissue and tumor areas. ResultsAlgorithmic analysis of 69 WSIs from rescanned aged slides vs. that of contemporaneous WSIs concluded 18 (28%) similar finding in tumor areas (within 10%), 56 (82%) had identicaltissue areas, and 54 (79%) had similar tumor percentages. ConclusionWSIs of aged H&E slides and stained paraffin block re-cuts produce different tumor quantification compared to those of original scanned sslides most likely due to pre-analytical factors. The difference in tumor area detected between original and rescanned WSIs trended upward in the period between 2012 and 2014. Less tumor area was detected as the slides age. Recut and H&E-stained tissues from stored paraffin blocks may detect more tumor due to excess eosinophilia. These results highlights the value of documenting archives of H&E WSIs collected at the procurement time. Such images provide a superior archive over glass slides and Formalin-Fixed Paraffin-Embedded (FFPE) blocks and contribute betterg to WSIs analysis application.