The potential of predictive and prognostic breast MRI (P2-bMRI)

Dietzel, Matthias; Trimboli, Rubina Manuela; Zanardo, Moreno; Schultz-Wendtland, Rüdiger; Uder, Michael; Clauser, Paola; Sardanelli, Francesco; Baltzer, Pascal

doi:10.1186/s41747-022-00291-z

Cited by 10 publications

(16 citation statements)

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“…Radiomics can extract features from medical images with high throughput and analyze them using mathematical algorithms, and is widely used in the diagnosis and prognosis prediction of breast diseases. [28][29][30] In this study, we extracted 10 features highly related to MBC, including 4 gray GLCM, 1 GLDM, 1 GLRLM, 2 GLSZM, and 2 NGTDM. These features are unrecognizable to the human eye.…”

Section: Discussionmentioning

confidence: 99%

The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses

Lyu,

Zhang,

Zhang

et al. 2023

J of Clinical Ultrasound

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BackgroundFemale breast cancer has surpassed lung cancer as the most common cancer, and is also the main cause of cancer death for women worldwide. Breast cancer <1 cm showed excellent survival rate. However, the diagnosis of minimal breast cancer (MBC) is challenging.ObjectiveThe purpose of our research is to develop and validate an radiomics model based on ultrasound images for early recognition of MBC.Methods302 breast masses with a diameter of <10 mm were retrospectively studied, including 159 benign and 143 malignant breast masses. The radiomics features were extracted from the gray‐scale ultrasound image of the largest face of each breast mass. The maximum relevance minimum reduncancy and recursive feature elimination methods were used to screen. Finally, 10 features with the most discriminating value were selected for modeling. The random forest was used to establish the prediction model, and the rad‐score of each mass was calculated. In order to evaluate the effectiveness of the model, we calculated and compared the area under the curve (AUC) value, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the model and three groups with different experience in predicting small breast masses, and drew calibration curves and decision curves to test the stability and consistency of the model.ResultsWhen we selected 10 radiomics features to calculate the rad‐score, the prediction efficiency was the best, the AUC values for the training set and testing set were 0.840 and 0.793, which was significantly better than the insufficient experience group (AUC = 0.673), slightly better than the moderate experience group (AUC = 0.768), and was inferior to the experienced group (AUC = 0.877). The calibration curve and decision curve also showed that the radiomics model had satisfied stability and clinical application value.ConclusionThe radiomics model based on ultrasound image features has a satisfied predictive ability for small breast masses, and is expected to become a potential tool for the diagnosis of MBC, and it is a zero cost (in terms of patient participation and imaging time).

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Section: Discussionmentioning

confidence: 99%

The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses

Lyu,

Zhang,

Zhang

et al. 2023

J of Clinical Ultrasound

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“…Die Tumorvaskularisation ist ein typisches Merkmal des Mammakarzinoms. Muster der Tumorvaskularisation gelten als Bildgebungsbiomarker des Mammakarzinoms [19]. Zur Analyse der Tumorvaskularisation hat sich die dynamische kontrastverstärkte (DCE) MRT etabliert.…”

Section: Mammakarzinomunclassified

“…etc.). Derartige Phänotypen, die aus der Bildgebung abgeleitet werden, werden durch das Breast Imaging Reporting and Documentation System objektiviert (BI-RADS) [19]. Aus dem BI-RADS MRT-Befund kann der Kaiser-Score ermittelt werden.…”

Section: Mammakarzinomunclassified

“…Digital mammography (including more advanced techniques such as digital tomosynthesis and contrast-enhanced mammography), ultrasound and MRI are the main methods of breast diagnostic imaging [17,18]. Imaging biomar-kers of breast carcinoma are widely used in both routine clinical practice and scientific research [19]. In this section we present essential imaging biomarkers of breast cancer and discuss their future development using selected examples.…”

Section: Breast Carcinomamentioning

confidence: 99%

“…etc.). Such phenotypes derived from imaging are objectified by the Breast Imaging Reporting and Documentation System (BI-RADS) [19]. The Kaiser score can be determined from the BI-RADS MRI finding.…”

Section: Breast Carcinomamentioning

confidence: 99%

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Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma

Bäuerle,

Dietzel,

Pinker

et al. 2023

Rofo

Self Cite

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Background Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric). Method This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality. Results and Conclusion Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers. Key Points: Citation Format

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