Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer

Dietzel, Matthias; Schulz‐Wendtland, Rüdiger; Ellmann, Stephan; Zoubi, R; Wenkel, Evelyn; Hammon, Matthias; Clauser, Paola; Uder, Michael; Runnebaum, I. B.; Baltzer, Pascal

doi:10.1038/s41598-020-60393-9

Cited by 18 publications

(33 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another study by Illan et al [42] focused on the clinically challenging non-mass lesions in bMRI and provided automatic segmentation, aiding visual analysis of contrast enhancement kinetics for inexperienced and expert readers. Next to facilitating lesion characterization, a radiomics method incorporating prior knowledge on physiological enhancement characteristics has been shown useful for predicting survival in patients with primary breast cancer, based on automatically extracted contrast enhancement kinetics and volumetric features [43]. Vascular properties can be quantified by DCE measurements including pharmacokinetic mapping.…”

Section: Discussionmentioning

confidence: 99%

An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies

et al. 2021

Self Cite

View full text Add to dashboard Cite

Objectives Due to its high sensitivity, DCE MRI of the breast (bMRI) is increasingly used for both screening and assessment purposes. The high number of detected lesions poses a significant logistic challenge in clinical practice. The aim was to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions and thereby avoid unnecessary biopsies. Methods This retrospective study included consecutive patients with MRI-suspicious findings (BI-RADS 4/5). Two blinded readers analyzed DCE images using a commercially available software, automatically extracting BI-RADS curve types and pharmacokinetic enhancement features. After principal component analysis (PCA), a neural network–derived A.I. classifier to discriminate benign from malignant lesions was constructed and tested using a random split simple approach. The rate of avoidable biopsies was evaluated at exploratory cutoffs (C1, 100%, and C2, ≥ 95% sensitivity). Results Four hundred seventy (295 malignant) lesions in 329 female patients (mean age 55.1 years, range 18–85 years) were examined. Eighty-six DCE features were extracted based on automated volumetric lesion analysis. Five independent component features were extracted using PCA. The A.I. classifier achieved a significant (p < .001) accuracy to distinguish benign from malignant lesion within the test sample (AUC: 83.5%; 95% CI: 76.8–89.0%). Applying identified cutoffs on testing data not included in training dataset showed the potential to lower the number of unnecessary biopsies of benign lesions by 14.5% (C1) and 36.2% (C2). Conclusion The investigated automated 4D radiomics approach resulted in an accurate A.I. classifier able to distinguish between benign and malignant lesions. Its application could have avoided unnecessary biopsies. Key Points • Principal component analysis of the extracted volumetric and temporally resolved (4D) DCE markers favored pharmacokinetic modeling derived features. • An A.I. classifier based on 86 extracted DCE features achieved a good to excellent diagnostic performance as measured by the area under the ROC curve with 80.6% (training dataset) and 83.5% (testing dataset). • Testing the resulting A.I. classifier showed the potential to lower the number of unnecessary biopsies of benign breast lesions by up to 36.2%, p < .001 at the cost of up to 4.5% (n = 4) false negative low-risk cancers.

show abstract

Section: Discussionmentioning

confidence: 99%

An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our approach was simplified in restricting the radiologists´influence to recognition and a VOI-based encirclement of the tumor and a subsequent sub-automatic segmentation by the machine learning algorithm, using a simple threshold of 30% of the highest intensity inside the volume to obtain an automatic fine segmentation of the tumor mass. A similar segmentation approach was adopted recently by Dietzel et al [34]. By thresholding voxels in the initial enhancement in the dynamics having more than 30% difference they identify the active tumor tissue and extract vascularization patterns.…”

Section: Discussionmentioning

confidence: 99%

A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer

et al. 2020

View full text Add to dashboard Cite

Background Recently, radiomics has emerged as a non-invasive, imaging-based tissue characterization method in multiple cancer types. One limitation for robust and reproducible analysis lies in the inter-reader variability of the tumor annotations, which can potentially cause differences in the extracted feature sets and results. In this study, the diagnostic potential of a rapid and clinically feasible VOI (Volume of Interest)-based approach to radiomics is investigated to assess MR-derived parameters for predicting molecular subtype, hormonal receptor status, Ki67-and HER2-Expression, metastasis of lymph nodes and lymph vessel involvement as well as grading in patients with breast cancer. Methods A total of 98 treatment-naïve patients (mean 59.7 years, range 28.0-89.4) with BI-RADS 5 and 6 lesions who underwent a dedicated breast MRI prior to therapy were retrospectively included in this study. The imaging protocol comprised dynamic contrast-enhanced T1weighted imaging and T2-weighted imaging. Tumor annotations were obtained by drawing VOIs around the primary tumor lesions followed by thresholding. From each segmentation, 13.118 quantitative imaging features were extracted and analyzed with machine learning methods. Validation was performed by 5-fold cross-validation with 25 repeats. Results Predictions for molecular subtypes obtained AUCs of 0.75 (HER2-enriched), 0.73 (triplenegative), 0.65 (luminal A) and 0.69 (luminal B). Differentiating subtypes from one another was highest for HER2-enriched vs triple-negative (AUC 0.97), followed by luminal B vs triple-negative (0.86). Receptor status predictions for Estrogen Receptor (ER), Progesterone

show abstract

“…3 a Oben werden Auszüge aus den auf dem Radiomics Workflow basierenden MRI-Daten der Brust dargestellt. Es wurde eine volumetrische Analyse dynamischer Anreicherungsparameter durchgeführt, die Surrogate der Tumorheterogenität und der Krebsvaskularität liefert [17]. Solche Parameter sind eng mit der Pathophysiologie verbunden und erleichtern die Interpretation des radiomischen Modells; ein Schritt, der mit klassischen Texturparametern nicht immer möglich ist.…”

Section: ▶ Abb 2 Stelzer Et Al Untersuchten Die Radiomischen Signatunclassified

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

Dietzel

Clauser

Kapetas

et al. 2021

Rofo

Self Cite

View full text Add to dashboard Cite

Background Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology “imaging biomarker”, “radiomics”, and “artificial intelligence” are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information. Methods and Results This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed. Conclusion Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine. Key Points: Citation Format

show abstract

Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer

Cited by 18 publications

References 53 publications

An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies

An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies

A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

Contact Info

Product

Resources

About