The Clinical Significance and Molecular Features of the Spatial Tumor Shapes in Breast Cancers

The effect of total size, area, and volume of lesions in multifocal/multicentric breast cancers and unifocal breast cancers on survival: An observational study

In this study, we aimed to investigate the prognostic effect of the classifications made according to the stage of the largest lesion diameter (T-max stage) and of the sum of the longest diameters of the lesions (T-sum stage), the largest area stage (A-max stage), the sum of the largest areas (A-sum stage), the highest volume stage (V-max stage), the sum of the highest volume (V-sum stage) on disease-free survival, and overall survival (OS) in multifocal/multicentric breast cancers (MMBCs) and unifocal breast cancers (UBCs). The study included a total of 769 patients either with MMBC (n = 128) or UBC (n = 641) who underwent surgery between 2006 and 2015. In the analysis, the median age of 769 patients was 53.0 (20.0-94.0) years, and 16.6% of these 769 patients were MMBC and 83.4% were UBC. In multivariate analysis, lymphovascular invasion (LVİ), estrogen receptor, and nodal status were common independent prognostic factors, whereas T-max stage [(HR: 1.17) (CI 95%: 1.03-1.33) (P = .018)] was a prognostic factor for OS. In multivariate analysis, the T-max stage is an independent risk factor for OS. Therefore, T-max should be continued to be used for measurement and T-stage should be used for classification in MMBCs/UBCs.

Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features

PURPOSE

Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors.

MATERIAL AND METHODS

A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority.

RESULTS

The study enrolled 234 female patients with a mean age of 50.2 years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set.

CONCLUSION

This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.

Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume

Background

Prostate tumor volume predicts biochemical recurrence, metastases, and tumor proliferation. A recent study showed that prostate tumor eccentricity (elongation or roundness) correlated with Gleason score. No studies examined the relationship among the prostate tumor's shape, volume, and potential aggressiveness.

Methods

Of the 26 patients that were analyzed, 18 had volumes >1 cc for the histology-based study, and 25 took up contrast material for the MRI portion of this study. This retrospective study quantitatively compared tumor eccentricity and volume measurements from pathology assessment sectioned wholemount prostates and multi-parametric MRI to Gleason scores. Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Pixel-based blobbing, and labeling were applied to digitized pathology slides and threshold ACE images. Tumor volumes were measured by counting voxels within the blob. Eccentricity calculation used moments of inertia from the blobs.

Results

From wholemount prostatectomy slides, fitting two sets of independent variables, prostate tumor eccentricity (largest blob eccentricity, weighted eccentricity, filtered weighted eccentricity) and tumor volume (largest blob volume, average blob volume, filtered average blob volume) to Gleason score in a multivariate analysis, yields correlation coefficient R=0.798 to 0.879 with P<0.01. The eccentricity t-statistic exceeded the volume t-statistic. Fitting histology-based total prostate tumor volume against Gleason score yields R=0.498, P=0.0098. From multi-parametric MRI, the correlation coefficient R between the Gleason score and the largest blob eccentricity for varying thresholds (0.30 to 0.55) ranged from -0.51 to -0.672 (P<0.01). For varying thresholds (0.60 to 0.80) for MRI detection, the R between the largest blob volume eccentricity against the Gleason score ranged from 0.46 to 0.50 (P<0.03). Combining tumor eccentricity and tumor volume in multivariate analysis failed to increase Gleason score prediction.

Conclusions

Prostate tumor eccentricity, determined by histology or MRI, more accurately predicted Gleason score than prostate tumor volume. Combining tumor eccentricity with volume from histology-based analysis enhanced Gleason score prediction, unlike MRI.

Calsequestrin 2 overexpression in breast cancer increases tumorigenesis and metastasis by modulating the tumor microenvironment

The spatial tumor shape is determined by the complex interactions between tumor cells and their microenvironment. Here, we investigated the role of a newly identified breast cancer-related gene, calsequestrin 2 (CASQ2), in tumor-microenvironment interactions during tumor growth and metastasis. We analyzed gene expression and three-dimensional tumor shape data from the breast cancer dataset of The Cancer Genome Atlas (TCGA) and identified CASQ2 as a potential regulator of tumor-microenvironment interaction. In TCGA breast cancer cases containing information of three-dimensional tumor shapes, CASQ2 mRNA showed the highest correlation with the spatial tumor shapes. Furthermore, we investigated the expression pattern of CASQ2 in human breast cancer tissues. CASQ2 was not detected in breast cancer cell lines in vitro but was induced in the xenograft tumors and human breast cancer tissues. To evaluate the role of CASQ2, we established CASQ2-overexpressing breast cancer cell lines for in vitro and in vivo experiments. CASQ2 overexpression in breast cancer cells resulted in a more aggressive phenotype and altered epithelial-mesenchymal transition (EMT) markers in vitro. CASQ2 overexpression induced cancer-associated fibroblast characteristics along with increased hypoxia-inducible factor 1α (HIF1α) expression in stromal fibroblasts. CASQ2 overexpression accelerated tumorigenesis, induced collagen structure remodeling, and increased distant metastasis in vivo. CASQ2 conferred more metaplastic features to triple-negative breast cancer cells. Our data suggest that CASQ2 is a key regulator of breast cancer tumorigenesis and metastasis by modulating diverse aspects of tumor-microenvironment interactions.

Correlation of prostate tumor eccentricity and Gleason scoring from prostatectomy and multi-parametric-magnetic resonance imaging

Background

Proliferating cancer cells interacting with their microenvironment affects a tumor's spatial shape. Elongation or roundness (eccentricity) of lung, skin, and breast cancers indicates the cancer's relative aggressiveness. Non-invasive determination of the prostate tumor's shape should provide meaningful input for prognostication and clinical management. There are currently few studies of prostate tumor shape, therefore this study examines the relationship between a prostate tumor's eccentricity, derived from spatially registered multi-parametric MRI and histology slides, and Gleason scores.

Methods

A total of 26 consecutive patients were enrolled in the study. Median patient age was 60 years (range, 49 to 75 years), median PSA was 5.8 ng/mL (range, 2.3 to 23.7 ng/mL, and median Gleason score was 7 (range, 6 to 9). Multi-parametric MRI (T1, T2, Diffusion, Dynamic Contrast Enhanced) were resampled, rescaled, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Also, tumor shape was computed from the histology slides. Blobbing, labeling, and calculation of eccentricity using moments of inertia were applied to the multi-parametric MRI and histology slides. The eccentricity measurements were compared to the Gleason scores from 25 patients.

Results

From histology slides analysis: the correlation coefficient between the eccentricity for the largest blob and a weighted average eccentricity against the Gleason score ranged from -0.67 to -0.78 for all 18 patients whose tumor volume exceeded 1.0 cc. From multi-parametric MRI analysis: the correlation coefficient between the eccentricity for the largest blob for varying thresholds against the Gleason score ranged from -0.60 to -0.66 for all 25 patients showing contrast uptake in the Dynamic Contrast Enhancement (DCE) MRI.

Conclusions

Spherical shape prostate adenocarcinoma shows a propensity for higher Gleason score. This novel finding follows lung and breast adenocarcinomas but depart from other primary tumor types. Analysis of multi-parametric MRI can non-invasively determine the prostate tumor's morphology and add critical information for prognostication and disease management. Eccentricity of smaller tumors (<1.0 cc) from MP-MRI correlates well with Gleason score, unlike eccentricity measured using histology of wholemount prostatectomy.

Modeling breast cancer survival and metastasis rates from moderate-sized clinical data

Predicting time-dependent survival probability of a breast cancer patient using information such as primary tumor size, grade, node spread status, and patient age at the time of surgery can be of immense help in managing life expectations and strategizing postoperative treatment. However, for moderate-sized clinical datasets the application of standard Kaplan-Meier theory to determine survival probability as a function of multiple cofactors can become challenging when continuous variables like tumor diameter and survival time are segmented into a large number of narrow intervals, a problem commonly termed the curse of dimensionality. We circumvent this problem by modeling the patient-to-patient distribution of primary tumor diameter with a realistic, right-skewed function, and then matching the diameter-marginalized survival with the mean Kaplan-Meier survival for the data. We apply this procedure on a recent clinical data from 1875 breast cancer patients and develop parameters that can be readily used to estimate post-surgery survival for an arbitrary time length. Finally, we show that the observed fraction of node-positive patients can be quantitatively explained within a simple tumor growth and metastasis framework. Employing two different tumor growth models from the literature (i.e., Gompertz and logistic growth models), we utilize the observed fraction-node-positive data to determine metastasis rates from the surface of a primary tumor and its patient-to-patient distribution.

Unification of aggregate growth models by emergence from cellular and intracellular mechanisms

Multicellular aggregate growth is regulated by nutrient availability and removal of metabolites, but the specifics of growth dynamics are dependent on cell type and environment. Classical models of growth are based on differential equations. While in some cases these classical models match experimental observations, they can only predict growth of a limited number of cell types and so can only be selectively applied. Currently, no classical model provides a general mathematical representation of growth for any cell type and environment. This discrepancy limits their range of applications, which a general modelling framework can enhance. In this work, a hybrid cellular Potts model is used to explain the discrepancy between classical models as emergent behaviours from the same mathematical system. Intracellular processes are described using probability distributions of local chemical conditions for proliferation and death and simulated. By fitting simulation results to a generalization of the classical models, their emergence is demonstrated. Parameter variations elucidate how aggregate growth may behave like one classical growth model or another. Three classical growth model fits were tested, and emergence of the Gompertz equation was demonstrated. Effects of shape changes are demonstrated, which are significant for final aggregate size and growth rate, and occur stochastically.

Growth pattern can be used as a new characteristic to predict malignancy in breast cancer

BACKGROUND

To date, anatomic tumor length is a key criterion for cancer staging and can be used to evaluate the effectiveness of therapies. This article describes growth pattern that can be used as a new characteristic to represent disease burden and tumor features and predict lymphatic metastasis.

METHODS

Patients with breast cancer were included in this 10-year (1999-2008) hospital-based multicenter retrospective study. The pathologic length/height ratio was used to illustrate the correlation between tumor features, behaviors and treatments in breast malignancies. The most appropriate ratio was chosen based on the comprehensive evaluation of p value and changing trend of each characteristic.

RESULTS

The sample consisted of 4211 women diagnosed with breast cancer. Among them, 2037 patients with complete pathologic length, width and height information were included in the final analysis. There were 2.34 ± 4.77 metastatic lymph nodes for spheroid tumors and 3.21 ± 5.82 for ellipsoid tumors when the cutoff point was 2. In addition, the proportion of ellipsoidal tumors gradually increased from 54.36 to 56.67% in the upper outer quadrant (UOQ) and from 6.7 to 9.03% in the central region with an increase in the cutoff point. The proportion of ER + PR + ellipsoid tumors significantly decreased from 50.1 to 45.35% and that of ER-PR ellipsoid tumors significantly increased from 32.73 to 36.24% with an increase in the cutoff point. Additionally, the best length/weight ratio to distinguish spheroid and ellipsoid tumors was 2.

CONCLUSION

This study described for the first time how growth pattern is correlated with tumor malignancy and how it influences the selection of therapeutic strategies for patients.

Timescale of tumor volume of a young breast cancer patient with luminal B subtype: A case report

RATIONALE

It is largely unknown about the tumor growth of breast cancer naturally. We devised and analyzed an appropriate mathematical tool of the equations that describe how fast tumors grow without treatment on the basis of the ellipsoid shape of solid breast cancer.

PATIENT CONCERNS

A 31-year-old woman presented with a painless palpable lump in her left breast for 5 months.

DIAGNOSIS

Infiltrated ductal breast cancer (histologic grade II) of luminal B INTERVENTIONS:: The patient did not receive any therapy due to her private reasons for 2 years, the analysis of the tumor volume growth was done regarding the growth rate of the tumor in the absence of intervention.

OUTCOMES

After 2 years of diagnosis of breast cancer, the tumor mass occupied the whole left breast with skin implanted and nipple abnormality. As this case indicated that the tumor's early growth rate was very slow. When the tumor volume reached 300 cm, its fast growth began without treatment. Later growth approached the maximum, when the tumor volume was more than 800 cm.

LESSONS

The tumor growth is segmental without therapy. Early diagnosis and treatment is the key to good prognosis for every breast cancer patient.

Phenotypic Plasticity of Fibroblasts during Mammary Carcinoma Development

Cancer-associated fibroblasts (CAFs) in the tumor microenvironment contribute to all stages of tumorigenesis and are usually considered to be tumor-promoting cells. CAFs show a remarkable degree of heterogeneity, which is attributed to developmental origin or to local environmental niches, resulting in distinct CAF subsets within individual tumors. While CAF heterogeneity is frequently investigated in late-stage tumors, data on longitudinal CAF development in tumors are lacking. To this end, we used the transgenic polyoma middle T oncogene-induced mouse mammary carcinoma model and performed whole transcriptome analysis in FACS-sorted fibroblasts from early- and late-stage tumors. We observed a shift in fibroblast populations over time towards a subset previously shown to negatively correlate with patient survival, which was confirmed by multispectral immunofluorescence analysis. Moreover, we identified a transcriptomic signature distinguishing CAFs from early- and late-stage tumors. Importantly, the signature of early-stage CAFs correlated well with tumor stage and survival in human mammary carcinoma patients. A random forest analysis suggested predictive value of the complete set of differentially expressed genes between early- and late-stage CAFs on bulk tumor patient samples, supporting the clinical relevance of our findings. In conclusion, our data show transcriptome alterations in CAFs during tumorigenesis in the mammary gland, which suggest that CAFs are educated by the tumor over time to promote tumor development. Moreover, we show that murine CAF gene signatures can harbor predictive value for human cancer.

Three-dimensional tumor visualization of invasive breast carcinomas using whole-mount serial section histopathology: implications for tumor size assessment

PURPOSE

Linear tumor size (T-size) estimated with conventional histology informs breast cancer management. Previously we demonstrated significant differences in margin and focality estimates using conventional histology versus digital whole-mount serial sections (WMSS). Using WMSS we can measure T-size or volume. Here, we compare WMSS T-size with volume, and with T-size measured conventionally. We also compare the ellipsoid model for calculating tumor volume to direct, WMSS measurement.

METHODS

Two pathologists contoured regions of invasive carcinoma and measured T-size from both WMSS and (simulated) conventional sections in 55 consecutive lumpectomy specimens. Volume was measured directly from the contours. Measurements were compared using the paired t-test or Spearman's rank-order correlation. A five-point 'border index' was devised and assigned to each case to parametrize tumor shape considering 'compactness' or cellularity. Tumor volumes calculated assuming ellipsoid geometry were compared with direct, WMSS measurements.

RESULTS

WMSS reported significantly larger T-size than conventional histology in the majority of cases [61.8%, 34/55; means = (2.34 cm; 1.99 cm), p < 0.001], with a 16.4% (9/55) rate of 'upstaging'. The majority of discordances were due to undersampling. T-size and volume were strongly correlated (r = 0.838, p < 0.001). Significantly lower volume was obtained with WMSS versus ellipsoid modeling [means = (1.18 cm³; 1.45 cm³), p < 0.001].

CONCLUSIONS

Significantly larger T-size is measured with WMSS than conventionally, due primarily to undersampling in the latter. Volume and linear size are highly correlated. Diffuse tumors interspersed with normal or non-invasive elements may be sampled less extensively than more localized masses. The ellipsoid model overestimates tumor volume.

Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction

Purpose To investigate whether radiomic features at MRI improve survival prediction in patients with glioblastoma multiforme (GBM) when they are integrated with clinical and genetic profiles. Materials and Methods Data in patients with a diagnosis of GBM between December 2009 and January 2017 (217 patients) were retrospectively reviewed up to May 2017 and allocated to training and test sets (3:1 ratio). Radiomic features (n = 796) were extracted from multiparametric MRI. A random survival forest (RSF) model was trained with the radiomic features along with clinical and genetic profiles (O-6-methylguanine-DNA-methyltransferase promoter methylation and isocitrate dehydrogenase 1 mutation statuses) to predict overall survival (OS) and progression-free survival (PFS). The RSF models were validated on the test set. The incremental values of radiomic features were evaluated by using the integrated area under the receiver operating characteristic curve (iAUC). Results The 217 patients had a mean age of 57.9 years, and there were 87 female patients (age range, 22-81 years) and 130 male patients (age range, 17-85 years). The median OS and PFS of patients were 352 days (range, 20-1809 days) and 264 days (range, 21-1809 days), respectively. The RSF radiomics models were successfully validated on the test set (iAUC, 0.652 [95% confidence interval {CI}, 0.524, 0.769] and 0.590 [95% CI: 0.502, 0.689] for OS and PFS, respectively). The addition of a radiomics model to clinical and genetic profiles improved survival prediction when compared with models containing clinical and genetic profiles alone (P = .04 and .03 for OS and PFS, respectively). Conclusion Radiomic MRI phenotyping can improve survival prediction when integrated with clinical and genetic profiles and thus has potential as a practical imaging biomarker. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Jain and Lui in this issue.

The prognostic role of tumor size in early breast cancer in the era of molecular biology

Background

The prognosis of early breast cancer (EBC) depends on patient and tumor characteristics. The association between tumor size, the largest diameter in TNM staging, and prognosis is well recognized. According to TNM, tumors classified as T2, could have very different volumes; e.g. a tumor of 2.1 cm has a volume of 4500 mm³, while a tumor of 4.9 cm has a volume of 60.000 mm³ even belonging to the same class. The aim of the study is to establish if the prognostic role of tumor size, expressed as diameter and volume, has been overshadowed by other factors.

Methods

The primary objective is to evaluate the association between tumor dimensions and overall survival (OS) / disease free survival (DFS), in our institution from January 1^st 2005 to September 30^th 2013 in a surgical T1-T2 population. Volume was evaluated with the measurement of three half-diameters of the tumor (a, b and c), and calculated using the following formula: 4/3π x a x b x c.

Results

341 patients with T1-T2 EBC were included. 86.5% were treated with conservative surgery. 85.1% had a Luminal subtype, 9.1% were Triple negative and 7.4% were HER2 positive. Median volume was 942 mm³ (range 0.52–31.651.2). 44 patients (12.9%) relapsed and 23 patients died. With a median follow-up of 6.5 years, the univariate analysis for DFS showed an association between age, tumor size, volume, histological grading and molecular subtype. The multivariate analysis confirmed the statistically significant association only for molecular subtype (p 0.005), with a worse prognosis for Triple negative and HER2 positive subtypes compared with Luminal (HR: 2.65; 95%CI: 1.34–5.22). Likewise for OS, an association was shown by the multivariate analysis solely for molecular subtype (HER2 and Triple negative vs. Luminal. HR: 2.83; 95% CI:1.46–5.49; p 0.002).

Conclusions

In our study, the only parameter that strongly influences survival is molecular subtype. These findings encourage clinicians to choose adjuvant treatment not based on dimensional criteria but on biological features.

Computer-aided evaluation of the correlation between MRI morphology and immunohistochemical biomarkers or molecular subtypes in breast cancer

Studies using tumor circularity (TC), a quantitative MRI morphologic index, to evaluate breast cancer are scarce. The purpose of this study is to evaluate the correlation between TC and immunohistochemical biomarkers or molecular subtypes in breast cancer. 146 patients with 150 breast cancers were selected. All tumors were confirmed by histopathology and examined by 3.0T MRI. TC was calculated by computer-aided software. The associations between TC and patient age, tumor size, histological grade, molecular subtypes, and immunohistochemical biomarkers including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 were analyzed. TC correlated inversely with tumor size (r = -0.224, P < 0.001), ER (r = -0.490, P < 0.001) and PR (r = -0.484, P < 0.001). However, TC correlated positively with Ki67 (r = 0.332, P < 0.001) and histological grade (r = 0.309, P < 0.001). In multiple linear regression analysis, tumor size, ER, PR and Ki67 were independent influential factors of TC. Compared with HER2-overexpressed (61.6%), luminal A (54.7%) and luminal B (52.3%) subtypes, triple-negative breast cancer (TNBC) showed the highest score of TC (70.8%, P < 0.001). Our study suggests that TC can be used as an imaging biomarker to predict the aggressiveness of newly diagnosed breast cancers. TNBC seems to present as an orbicular appearance when comparing with other subtypes.