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

Demircioğlu, Aydın; Grueneisen, Johannes; Ingenwerth, Marc; Hoffmann, Oliver; Pinker-Domenig, Katja; Morris, Elizabeth A.; Haubold, Johannes; Forsting, Michael; Nensa, Felix; Umutlu, Lale

doi:10.1371/journal.pone.0234871

Cited by 39 publications

(35 citation statements)

References 36 publications

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“…Apart from more recently introduced genomic and phenotypic characteristics, three features, grading, lymphonodular status and distant metastatic spread, are well-established biomarkers for prognosis. Comparable to previous publications, our prediction results for grading and lymph node metastases fell short in their diagnostic accuracy [8,34]. Similar to previously published results by Demircioglu et al from an MRI-based study, our results for grading amounted to an AUC of 0.78 (0.74, respectively) and 0.80 for lymph node metastases (0.71) [8].…”

Section: Discussionsupporting

confidence: 86%

“…Comparable to previous publications, our prediction results for grading and lymph node metastases fell short in their diagnostic accuracy [8,34]. Similar to previously published results by Demircioglu et al from an MRI-based study, our results for grading amounted to an AUC of 0.78 (0.74, respectively) and 0.80 for lymph node metastases (0.71) [8]. Only the determination of distant metastases achieved excellent AUC values of 0.96.…”

Section: Discussionsupporting

confidence: 85%

“…Over the past 5 years, numerous trials have been performed to assess the value and validity of radiomics in breast cancer characterization [8][9][10]. As recently reported in a rapid review on radiomics and breast cancer, most studies put the focus on breast MRI with the main emphasis on morphology, contrast enhancement kinetics and restricted diffusivity as features.…”

Section: Introductionmentioning

confidence: 99%

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Multiparametric Integrated 18F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding

Umutlu

Kirchner

Bruckmann

et al. 2021

Cancers

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Background: This study investigated the performance of simultaneous 18F-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype analysis, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. Methods: One hundred and twenty-four patients underwent simultaneous 18F-FDG PET/MRI. Breast tumors were segmented and radiomic features were extracted utilizing CERR software following the IBSI guidelines. LASSO regression was employed to select the most important radiomics features prior to model development. Five-fold cross validation was then utilized alongside support vector machines, resulting in predictive models for various combinations of imaging data series. Results: The highest AUC and accuracy for differentiation between luminal A and B was achieved by all MR sequences (AUC 0.98; accuracy 97.3). The best results in AUC for prediction of hormone receptor status and proliferation rate were found based on all MR and PET data (ER AUC 0.87, PR AUC 0.88, Ki-67 AUC 0.997). PET provided the best determination of grading (AUC 0.71), while all MR and PET analyses yielded the best results for lymphonodular and distant metastatic spread (0.81 and 0.99, respectively). Conclusion: 18F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for breast cancer phenotyping and tumor decoding, utilizing the perks of simultaneously acquired morphologic, functional and metabolic data.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

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Multiparametric Integrated 18F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding

Umutlu

Kirchner

Bruckmann

et al. 2021

Cancers

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“…Breast radiomics studies are mostly applied to the prediction of the molecular classification, lymph node metastasis and molecular markers of invasive ductal carcinoma. For example, Demircioglu A et al [ 52 ] constructed radiomics models for predicting Ki67 expression in invasive breast cancer based on eight features extracted from MRI images, with an AUC of 0.81. Zhou et al [ 53 ] explored the significance of MRI-radiomics models for predicting the expression of HER2 in patients with invasive breast cancer before surgery; the validation set AUC reached 0.81.…”

Section: Discussionmentioning

confidence: 99%

Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ

Zhao

Lin

et al. 2021

BMC Med Imaging

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Background The molecular biomarkers of breast ductal carcinoma in situ (DCIS) have important guiding significance for individualized precision treatment. This study was intended to explore the significance of radiomics based on ultrasound images to predict the expression of molecular biomarkers of mass type of DCIS. Methods 116 patients with mass type of DCIS were included in this retrospective study. The radiomics features were extracted based on ultrasound images. According to the ratio of 7:3, the data sets of molecular biomarkers were split into training set and test set. The radiomics models were developed to predict the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki67, p16, and p53 by using combination of multiple feature selection and classifiers. The predictive performance of the models were evaluated using the area under the curve (AUC) of the receiver operating curve. Results The investigators extracted 5234 radiomics features from ultrasound images. 12, 23, 41, 51, 31 and 23 features were important for constructing the models. The radiomics scores were significantly (P < 0.05) in each molecular marker expression of mass type of DCIS. The radiomics models showed predictive performance with AUC greater than 0.7 in the training set and test set: ER (0.94 and 0.84), PR (0.90 and 0.78), HER2 (0.94 and 0.74), Ki67 (0.95 and 0.86), p16 (0.96 and 0.78), and p53 (0.95 and 0.74), respectively. Conclusion Ultrasonic-based radiomics analysis provided a noninvasive preoperative method for predicting the expression of molecular markers of mass type of DCIS with good accuracy.

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“…Most papers deal with magnetic resonance (MRI) and the correlation between radiomic tumor features and the clinical data or histopathologic parameters. In another study [ 27 ], the authors presented a radiomic analysis of Volumes Of Interest (VOI) in MR images to predict molecular subtype, metastasis of lymph nodes, lymph vessel involvement, grading, hormonal receptor status, Ki67 and HER2 expression of breast lesions. The definition of new predictive models for the prediction of the histological and molecular characteristics of tumors based on radiomic analysis is a very interesting ongoing field of study that is providing encouraging results: this can play a key role in the prognostic evaluations that are crucial for oncological therapeutic decision-making.…”

Section: Introductionmentioning

confidence: 99%

Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome

et al. 2020

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Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with characteristics particularly suitable for breast cancer radiomic analysis. This work aims to evaluate radiomic features for predicting histological outcome and two cancer molecular subtypes, namely Human Epidermal growth factor Receptor 2 (HER2)-positive and triple-negative. From 52 patients, 68 lesions were identified and confirmed on histological examination. Radiomic analysis was performed on regions of interest (ROIs) selected from both low-energy (LE) and ReCombined (RC) CESM images. Fourteen statistical features were extracted from each ROI. Expression of estrogen receptor (ER) was significantly correlated with variation coefficient and variation range calculated on both LE and RC images; progesterone receptor (PR) with skewness index calculated on LE images; and Ki67 with variation coefficient, variation range, entropy and relative smoothness indices calculated on RC images. HER2 was significantly associated with relative smoothness calculated on LE images, and grading tumor with variation coefficient, entropy and relative smoothness calculated on RC images. Encouraging results for differentiation between ER+/ER−, PR+/PR−, HER2+/HER2−, Ki67+/Ki67−, High-Grade/Low-Grade and TN/NTN were obtained. Specifically, the highest performances were obtained for discriminating HER2+/HER2− (90.87%), ER+/ER− (83.79%) and Ki67+/Ki67− (84.80%). Our results suggest an interesting role for radiomics in CESM to predict histological outcomes and particular tumors’ molecular subtype.

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

Cited by 39 publications

References 36 publications

Multiparametric Integrated 18F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding

Multiparametric Integrated 18F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding

Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ

Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome

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