Experimental method and statistical analysis to fit tumor growth model using SPECT/CT imaging: a preclinical study

Impact of stroma remodeling on forces experienced by cancer cells and stromal cells within a pancreatic tumor tissue

Remodeling (re-engineering) of a tumor's stroma has been shown to improve the efficacy of anti-tumor therapies, without destroying the stroma. Even though it still remains unclear which stromal component/-s and what characteristics hinder the reach of nanoparticles deep into cancer cells, we hypothesis that mechanisms behind stroma's resistance to the penetration of nanoparticles rely heavily on extrinsic mechanical forces on stromal cells and cancer cells. Our hypothesis has been formulated on the basis of our previous study which has shown that changes in extracellular matrix (ECM) stiffness with tumor growth influence stresses exerted on fibroblasts and cancer cells, and that malignant cancer cells generate higher stresses on their stroma. This study attempts to establish a distinct identification of the components' remodeling on the distribution and magnitude of stress within a tumor tissue which ultimately will impact the resistance of stroma to treatment. In this study, our objective is to construct a three-dimensional in silico model of a pancreas tumor tissue consisting of cancer cells, stromal cells, and ECM to determine how stromal remodeling alters the stresses distribution and magnitude within the pancreas tumor tissue. Our results show that changes in mechanical properties of ECM significantly alter the magnitude and distribution of stresses within the pancreas tumor tissue. Our results revealed that these stresses are more sensitive to ECM properties as we see the stresses reaching to a maximum of 22,000 Pa for softer ECM with Young's modulus of 250 Pa. The stress distribution and magnitude within the pancreas tumor tissue does not show high sensitivity to the changes in mechanical properties of stromal cells surrounding stiffer cancer cells (PANC-1 with Young's modulus of 2400 Pa). However, softer cancer cells (MIA-PaCa-2 with (Young's modulus of 500 Pa) increase the stresses experienced by stiffer stromal cells and for stiffer ECM. By providing a unique platform to dissect and quantify the impact of individual stromal components on the stress distribution within a tumor tissue, this study serves as an important first step in understanding of which stromal components are vital for an efficient remodeling. This knowledge will be leveraged to overcome a tumor's resistance against the penetration of nanoparticles on a per-patient basis.

Modeling Physical Forces Experienced by Cancer and Stromal Cells Within Different Organ-Specific Tumor Tissue

Mechanical force exerted on cancer cells by their microenvironment have been reported to drive cells toward invasive phenotypes by altering cells' motility, proliferation, and apoptosis. These mechanical forces include compressive, tensile, hydrostatic, and shear forces. The importance of forces is then hypothesized to be an alteration of cancer cells' and their microenvironment's biophysical properties as the indicator of a tumor's malignancy state. Our objective is to investigate and quantify the correlation between a tumor's malignancy state and forces experienced by the cancer cells and components of the microenvironment. In this study, we have developed a multicomponent, three-dimensional model of tumor tissue consisting of a cancer cell surrounded by fibroblasts and extracellular matrix (ECM). Our results on three different organs including breast, kidney, and pancreas show that: A) the stresses within tumor tissue are impacted by the organ specific ECM's biophysical properties, B) more invasive cancer cells experience higher stresses, C) in pancreas which has a softer ECM (Young modulus of 1.0 kPa) and stiffer cancer cells (Young modulus of 2.4 kPa and 1.7 kPa) than breast and kidney, cancer cells experienced significantly higher stresses, D) cancer cells in contact with ECM experienced higher stresses compared to cells surrounded by fibroblasts but the area of tumor stroma experiencing high stresses has a maximum length of 40 μm when the cancer cell is surrounded by fibroblasts and 12 μm for when the cancer cell is in vicinity of ECM. This study serves as an important first step in understanding of how the stresses experienced by cancer cells, fibroblasts, and ECM are associated with malignancy states of cancer cells in different organs. The quantification of forces exerted on cancer cells by different organ-specific ECM and at different stages of malignancy will help, first to develop theranostic strategies, second to predict accurately which tumors will become highly malignant, and third to establish accurate criteria controlling the progression of cancer cells malignancy. Furthermore, our in silico model of tumor tissue can yield critical, useful information for guiding ex vivo or in vitro experiments, narrowing down variables to be investigated, understanding what factors could be impacting cancer treatments or even biomarkers to be looking for.

A multi-scale model for determining the effects of pathophysiology and metabolic disorders on tumor growth

The search for efficient chemotherapy drugs and other anti-cancer treatments would benefit from a deeper understanding of the tumor microenvironment (TME) and its role in tumor progression. Because in vivo experimental methods are unable to isolate or control individual factors of the TME and in vitro models often do not include all the contributing factors, some questions are best addressed with systems biology mathematical models. In this work, we present a new fully-coupled, agent-based, multi-scale mathematical model of tumor growth, angiogenesis and metabolism that includes important aspects of the TME spanning subcellular-, cellular- and tissue-level scales. The mathematical model is computationally implemented for a three-dimensional TME, and a double hybrid continuous-discrete (DHCD) method is applied to solve the governing equations. The model recapitulates the distinct morphological and metabolic stages of a solid tumor, starting with an avascular tumor and progressing through angiogenesis and vascularized tumor growth. To examine the robustness of the model, we simulated normal and abnormal blood conditions, including hyperglycemia/hypoglycemia, hyperoxemia/hypoxemia, and hypercarbia/hypocarbia - conditions common in cancer patients. The results demonstrate that tumor progression is accelerated by hyperoxemia, hyperglycemia and hypercarbia but inhibited by hypoxemia and hypoglycemia; hypocarbia had no appreciable effect. Because of the importance of interstitial fluid flow in tumor physiology, we also examined the effects of hypo- or hypertension, and the impact of decreased hydraulic conductivity common in desmoplastic tumors. The simulations show that chemotherapy-increased blood pressure, or reduction of interstitial hydraulic conductivity increase tumor growth rate and contribute to tumor malignancy.

Characteristics of contrast-enhanced ultrasonography and strain elastography of locally advanced breast cancer

BACKGROUND

Locally advanced breast cancer (LABC) is one of the subgroups of invasive breast cancer. The treatment of LABC is neoadjuvant chemotherapy (NAC) before surgery, which is different from the others. The study aimed to analyze and compare the characteristics of conventional ultrasound, contrast-enhanced ultrasonography (CEUS) and strain elastography (SE) in LABC patients who have different treatment outcomes, and to provide help for LABC in the imaging diagnosis and clinic treatment.

METHODS

From May 2018 to April 2019, 36 patients (40 lesions) of LABC were enrolled, which diagnosed by puncture biopsy. According to the clinical evaluation, these patients were recommended to undergo pre-operative NAC followed by surgery. All patients underwent conventional ultrasound, CEUS and SE before puncture. According to postoperative pathological grading and follow-up, the patients were divided into effective and ineffective groups. We summarized and compared the features of conventional ultrasound, CEUS and SE of patients in two groups. The correlation between the imaging characteristics and the postoperative pathological grading was also analyzed.

RESULTS

Conventional ultrasonic features of LABC: the most lesions of LABC were mass type (32/40, 80.0%), and all lesions were irregular. Most of lesions showed unclear boundaries (39/40, 97.5%), heterogeneous echoes (38/40, 95.0%), and internal calcifications (24/40, 60.0%). Most of lesions had hyperechoic halos (31/40, 77.5%), aspect ratio less than or equal to 1 (34/40, 85.0%), and blood flow grading was III (36/40, 90.0%). CEUS features of LABC: compared with the surrounding normal tissues, all the lesions (40/40,100.0%) were highly enhanced. Most of lesions (35/40, 87.5%) were uneven enhanced. The main enhancement mode was "fast in and slow out" (39/40, 97.5%). There were totally 25 lesions which had "solar sign" (25/40, 62.5%). SE features of LABC: the average visual elastography score of the lesions was 4.28±0.67, the maximum strain rate (E1) of the lesions averaged 4.88±0.54, and the overall strain rate of the lesion averaged 4.14±0.72. There was no significant difference between effective and ineffective groups in the characteristics of conventional ultrasound, CEUS and SE. There was a correlation between the appearance of "solar sign" in CEUS and postoperative pathological grading, and the contingency coefficient was 0.564 (P<0.05). The pathological grading of patients without solar sign was higher. The other characteristics of conventional ultrasound, CEUS and SE in LABC patients had no correlation with postoperative pathological grading.

CONCLUSIONS

In LABC, the conventional ultrasound usually shows irregular shape and unclear boundaries. The aspect ratio is less than or equal to 1. CEUS showed uneven enhancement of "fast in and slow out", and "solar sign" was often seen. Elastography showed that the texture of the lesion was significantly stiffer than the surrounding normal tissue. Ultrasound imaging before NAC had no relationship with pathological complete response or not. However, "solar sign" in CEUS was an important feature and had correlation with postoperative pathological grading.

The value of the computer-aided diagnosis system for thyroid lesions based on computed tomography images

Background

Thyroid nodules are commonly found at palpation amounting to 4-7% of the asymptomatic population and 50% of the cases are found at autopsy. Only a small proportion of thyroid nodules are malignant. The major challenge is the differential diagnosis of benign or malignant thyroid nodules, so we aim to develop the computer-assisted diagnostic method based on computed tomography (CT) images for thyroid lesions.

Methods

In this study, we retrospectively collected 52 benign and 46 malignant thyroid nodules from 90 patients in CT examinations, together with the pathologist findings and radiology diagnosis. The first-order statistic and gray-level co-occurrence matrix features were extracted from thyroid computed tomography images. These texture features were used to assess the malignancy risk of the thyroid nodules. Several classification algorithms, including support vector machine, linear discriminant analysis, random forest, and bootstrap aggregating, were applied in the prediction. Leave-one-out cross-validation was used to evaluate the performance of thyroid cancer recognition.

Results

In thyroid cancer identification based on a computed tomography image, we found the system using 17 texture features and support vector machine performed well. The accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, were 0.8673, 0.9105, 0.9130, 0.8269, 0.8235 and 0.9146, respectively.

Conclusions

The proposed computer-aided diagnosis system provides a good assessment of the malignancy-risk of the thyroid nodules, which may help radiologists to improve the accuracy and efficiency of thyroid diagnosis.