Breast and Tumour Volume Measurements in Breast Cancer Patients Using 3-D Automated Breast Volume Scanner Images

Improving lesion volume measurements on digital mammograms

Lesion volume is an important predictor for prognosis in breast cancer. However, it is currently impossible to compute lesion volumes accurately from digital mammography data, which is the most popular and readily available imaging modality for breast cancer. We make a step towards a more accurate lesion volume measurement on digital mammograms by developing a model that allows to estimate lesion volumes on processed mammogram. Processed mammograms are the images routinely used by radiologists in clinical practice as well as in breast cancer screening and are available in medical centers. Processed mammograms are obtained from raw mammograms, which are the X-ray data coming directly from the scanner, by applying certain vendor-specific non-linear transformations. At the core of our volume estimation method is a physics-based algorithm for measuring lesion volumes on raw mammograms. We subsequently extend this algorithm to processed mammograms via a deep learning image-to-image translation model that produces synthetic raw mammograms from processed mammograms in a multi-vendor setting. We assess the reliability and validity of our method using a dataset of 1778 mammograms with an annotated mass. Firstly, we investigate the correlations between lesion volumes computed from mediolateral oblique and craniocaudal views, with a resulting Pearson correlation of 0.93 [95% confidence interval (CI) 0.92 - 0.93]. Secondly, we compare the resulting lesion volumes from true and synthetic raw data, with a resulting Pearson correlation of 0.998 [95%CI 0.998 - 0.998] . Finally, for a subset of 100 mammograms with a malignant mass and concurrent MRI examination available, we analyze the agreement between lesion volume on mammography and MRI, resulting in an intraclass correlation coefficient of 0.81 [95%CI 0.73 - 0.87] for consistency and 0.78 [95%CI 0.66 - 0.86] for absolute agreement. In conclusion, we developed an algorithm to measure mammographic lesion volume that reached excellent reliability and good validity, when using MRI as ground truth. The algorithm may play a role in lesion characterization and breast cancer prognostication on mammograms.

Modeling the subcutaneous pharmacokinetics of antibodies co-administered with rHuPH20

Predicting the subcutaneous (SC) pharmacokinetics (PK) of antibodies in humans is challenging, with clinical data currently being the only reliable data source for modeling SC absorption and bioavailability. Recombinant human hyaluronidase PH20 (rHuPH20) is an enzyme that facilitates SC delivery of high-dose, high-volume therapeutics. Numerous monoclonal antibodies have been co-administered SC with rHuPH20 in a clinical setting, establishing an extensive PK database. The goal of this work is to demonstrate how aggregated clinical data can be leveraged in a universal modeling framework for characterizing SC antibody PK, resulting in parameterization that can be used in predictive simulations of new antibodies. Data for 10 individual antibodies co-administered SC with rHuPH20 were obtained from publicly available sources. PK modeling of each antibody was conducted using the same model structure, but uniquely parameterized. The model structure consisted of a two-compartment model to capture linear kinetics, plus a target-binding mechanism to accommodate nonlinear kinetics driven by antibody-target complex formation and elimination. The clinical PK profiles for all antibodies were accurately described using the universal modeling framework. The SC PK parameters of absorption and bioavailability were consistent across the range of antibody and target properties evaluated. SC administration with rHuPH20 yielded a 30% increase in absorption rate on average and similar or better bioavailability. These parameter values can serve as initial conditions for model-based PK predictions for new antibodies co-administered SC with rHuPH20 to enable evaluation of optimal SC dose and schedule regimens prior to and during clinical development.

Inter-observer variation in two-dimensional and three-dimensional ultrasound measurement of papillary thyroid microcarcinoma

BACKGROUNDS

The reliable ultrasound (US) measurements of papillary thyroid microcarcinoma (PTMC) are very important during active surveillance. This prospective study was design to investigate the inter-observer reliability and agreement of two- dimensional ultrasound(2DUS) and three-dimensional ultrasound(3DUS) in the measurement of maximum diameter and volume for PTMC.

METHODS

This prospective study included 51 consecutive patients with solitary PTMC confirmed by biopsy. Two independent observers performed measurements of each tumor using a standardized measurement protocol. The maximum diameter was the largest one of the three diameters measured on the largest transverse and longitudinal 2DUS images. 2DUS volume was calculated using ellipsoid formula method. The virtual organ computer aided analysis(VOCAL) was used to determine 3DUS volume. The inter-observer reliability was assessed using intraclass correlation coefficient(ICC) with 95% confidence intervals(CIs). Bland-Altman analysis was used to evaluate agreement, and expressed as a bias with 95% limits of agreement(LOA).

RESULTS

The maximum diameter was 0.78 ± 0.14 cm. Volume measured by 3DUS was significantly smaller than that by 2DUS(0.163 ± 0.074 cm3 vs. 0.175 ± 0.078 cm3, P = 0.005). The ICCs of inter-observer reliability of maximum diameter, 2DUS volume and 3DUS volume was 0.922(0.864-0.955), 0.928(0.874-0.959), and 0.974(0.955-0.985), respectively. The ICCs of 2DUS and 3DUS volume was 0.955(0.909-0.976). The inter-observer agreement of maximum diameter, 2DUS volume and 3DUS volume was 1.096(0.7322 to 1.459), 1.008(0.5802-1.435), and 1.011(0.7576-1.265), respectively. The inter-observer agreement of 2DUS and 3DUS volume was 1.096(0.7322 to 1.459).

CONCLUSION

Maximum diameter had the lowest degree of observer variation among all the measurements. Volume measured by 3DUS had lower variability and higher repeatability than that by 2DUS, which might be helpful to provide more reliable estimates of tumor size for PTMC.

Attention-Based Deep Learning System for Classification of Breast Lesions-Multimodal, Weakly Supervised Approach

Breast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private dataset with 1174 non-cancer and 794 cancer images labelled at the image level with pathological ground truth confirmation. We used feature extractors (ResNet-18, ResNet-34, ResNet-50 and EfficientNet-B0) pre-trained on ImageNet. The best results were achieved with multimodal-view classification using both CC and MLO images simultaneously, resized by half, with a patch size of 224 px and an overlap of 0.25. It resulted in AUC-ROC = 0.896 ± 0.017, F1-score 81.8 ± 3.2, accuracy 81.6 ± 3.2, precision 82.4 ± 3.3, and recall 81.6 ± 3.2. Evaluation with the Chinese Mammography Database, with 5-fold cross-validation, patient-wise breakdowns, and transfer learning, resulted in AUC-ROC 0.848 ± 0.015, F1-score 78.6 ± 2.0, accuracy 78.4 ± 1.9, precision 78.8 ± 2.0, and recall 78.4 ± 1.9. The CLAM algorithm's attentional maps indicate the features most relevant to the algorithm in the images. Our approach was more effective than in many other studies, allowing for some explainability and identifying erroneous predictions based on the wrong premises.

Evaluation of a new method of calculating breast tumor volume based on automated breast ultrasound

Objective

To evaluate the effectiveness and advantages of a new method for calculating breast tumor volume based on an automated breast ultrasound system (ABUS).

Methods

A total of 42 patients (18–70 years old) with breast lesions were selected for this study. The Ivenia ABUS 2.0 (General Electric Company, USA) was used, with a probe frequency of 6–15 MHz. Adobe Photoshop CS6 software was used to calculate the pixel ratio of each ABUS image, and to draw an outline of the tumor cross-section. The resulting area (in pixels) was multiplied by the pixel ratio to yield the area of the tumor cross-section. The Wilcoxon signed rank test and Bland-Altman plot were used to compare mean differences and mean values, respectively, between the two methods.

Results

There was no significant difference between the tumor volumes calculated by pixel method as compared to the traditional method (P>0.05). Repeated measurements of the same tumor volume were more consistent with the pixel method.

Conclusion

The new pixel method is feasible for measuring breast tumor volume and has good validity and measurement stability.

Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers

Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, normalized distance from 18F-fluorodeoxyglucose (18F-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using 18F-FDG PET-computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of 18F-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non-small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NHOC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome.

Relationship between formulaic breast volume and risk of breast cancer based on linear measurements

BACKGROUND

Whether breast volume is a risk factor for breast cancer is controversial. This study aimed to evaluate whether a significant association between breast volume and risk of breast cancer, based on linear measurements, was present by applying propensity score matching (PSM).

METHODS

The study was designed as a hospital-based case-control study. Between March 2018 and May 2019, 208 cases and 340 controls were retrospectively reviewed. Information on menarche, smoking, feeding mode, oral contraceptives, reproductive history and family history was obtained through a structured questionnaire. Breast volume was calculated using a formula based on linear measurements of breast parameters. Cox regression and PSM were used to estimate odds ratios and 95% confidence intervals for breast cancer using risk factors adjusted for potential confounders.

RESULTS

There was a significant difference in breast volume between the two groups before propensity score matching (P = 0.014). Binary logistic regression showed that the risk of breast cancer was slightly higher in the case group with larger breast volumes than in the control group(P = 0.009, OR = 1.002, 95%CI:1.000 ~ 1.003). However, there was no significant statistical difference between the two groups using an independent sample Mann-Whitney U test (P = 0.438) or conditional logistic regression (P = 0.446).

CONCLUSIONS

After PSM for potential confounding factors, there is no significant difference in breast volume estimated by BREAST-V formula between the case group and the control group. The risk of breast cancer may not be related to breast volume in Chinese women.