Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs

Siviengphanom, Somphone; Gandomkar, Ziba; Lewis, Sarah; Brennan, Patrick C.

doi:10.1016/j.acra.2021.09.025

Cited by 24 publications

(15 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Encouraging results have been reported in the use of other imaging methods in the diagnosis and characterization of breast tumors, suggesting the potential clinical value of mammography-based radiomics. However, in a recently published scoping review in which predicting breast cancer characteristics with radiomics is included, the final conclusion was that further efforts are required to standardize radiomics and select relevant mammographic radiomic features [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status

et al. 2023

View full text Add to dashboard Cite

There are different breast cancer molecular subtypes with differences in incidence, treatment response and outcome. They are roughly divided into estrogen and progesterone receptor (ER and PR) negative and positive cancers. In this retrospective study, we included 185 patients augmented with 25 SMOTE patients and divided them into two groups: the training group consisted of 150 patients and the validation cohort consisted of 60 patients. Tumors were manually delineated and whole-volume tumor segmentation was used to extract first-order radiomic features. The ADC-based radiomics model reached an AUC of 0.81 in the training cohort and was confirmed in the validation set, which yielded an AUC of 0.93, in differentiating ER/PR positive from ER/PR negative status. We also tested a combined model using radiomics data together with ki67% proliferation index and histological grade, and obtained a higher AUC of 0.93, which was also confirmed in the validation group. In conclusion, whole-volume ADC texture analysis is able to predict hormonal status in breast cancer masses.

show abstract

Section: Discussionmentioning

confidence: 99%

Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Moreover, textural features (involving first-and secondorder gray level co-occurrence matrix/GLCM-based statistics) have been shown to be helpful in identifying the global gist of natural scenes [21][22][23][24]. However, it is unknown if a set of these global features from mammograms using radiomics approach [25] can distinguish the strongly perceived (i.e., high-gist) from the poorly perceived gist of breast cancer images (i.e., low-gist). We assume that there are differences in the global radiomic features between the highest and lowest of gist images (concentrating only on these categories to enable a strong distinction between them).…”

Section: Introductionmentioning

confidence: 99%

Predicting the gist of breast cancer on a screening mammogram using global radiomic features

Siviengphanom,

Lewis,

Brennan

et al. 2024

Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment

Self Cite

View full text Add to dashboard Cite

This study explored if using a set of global radiomic (i.e., computer-extracted) features derived from mammograms could predict the gist of breast cancer (holistic perceptual information provided from radiologists' first impression about the presence of an image abnormality after a brief sight of the image). A retrospective de-identified dataset was used to collect the gist of breast cancer (i.e., gist scores) from 13 readers interpreting 1100 screening craniocaudal mammograms (659 current "normal" cancer-free images, and 441 "prior" no visible signs of cancer images acquired two years before current cancer mammograms). The collected gist scores from all readers were averaged to eliminate the noise of the gist signal, giving one gist score per image. The images were grouped into high-and low-gist based on the 75 th and 25 th percentiles of the images containing the highest and lowest gist scores, respectively. A set of 130 handcrafted global radiomic features per image were extracted and used to construct two machine learning random forest classifiers: 1). Normal and 2). Prior based on the corresponding features computed from the "normal" and "prior" images, for distinguishing high-from lowgist images. The classifiers were trained and validated using the 10-fold cross-validation approach and their performances were measured by the area under the receiver operating characteristic curve (AUC). The Normal and Prior classifiers resulted in AUCs of 0.83 (95% CI: 0.77-0.85) and 0.84 (95% CI: 0.80-0.87) respectively, suggesting that the global mammographic radiomic features can predict the gist of breast cancer on a screening mammogram.

show abstract

“…Earlier studies showed that the global radiomic signatures from screening mammography can predict a future breast cancer with an accuracy level more than conventional breast cancer risk prediction models such as Gail or Tyrer-Cuzick models [8][9][10][11]. It was also shown that this global signature of malignancy (also known as gist signal) is detected by radiologists after a half-second image presentation [12] and the strength of this signal is not associated with the mammographic breast density or lesion size [13].…”

Section: Introductionmentioning

confidence: 99%

False-negative diagnosis might occur due to absence of the global radiomic signature of malignancy on screening mammograms

Gandomkar

Lewis

Siviengphanom³

et al. 2023

Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment

View full text Add to dashboard Cite

The global radiomic signature extracted from mammograms can indicate that malignancy appearances are present within an image. This study focuses on a set of 129 screen-detected breast malignancies, which were also visible on the prior screening examinations (i.e., missed cancers based on the priors). All cancer signs on the prior examinations were actionable based on the opinion of a panel of three experienced radiologists, who retrospectively interpreted the prior examinations (knowing that a later screening round had revealed a cancer). We investigated if the global radiomic signature could differentiate between screening rounds: when the cancer was detected (“identified cancers”), from the round immediately before (“missed cancers”). Both identified cancers and “missed cancers” were collected using a single vendor technology. A set of “normals”, matched based on mammography units, was also retrieved from a screening archive. We extracted a global radiomic signature, containing first and second-order statistics features. Three classification tasks were considered: (1) “identified cancers” vs “missed cancers”, (2) “identified cancers” vs “normals”, (3) “missed cancers” vs “normal”. To train and validate the models, leave-one-case-out cross-validation was used. The classifier resulted in an AUC of 0.66 (95%CI=0.60-0.73, P<0.05) for “missed cancers” vs “identified cancers” and an AUC of 0.65 (95%CI=0.60-0.69, P<0.05) for “normals” vs “identified cancers”. However, the AUC of the classifier for differentiating “normals” from “missed cancers” was at chance-level (AUC=0.53 (95%CI=0.48-0.58, P=0.23). Therefore, eliminating some of these “missed” cancers in clinical practice would be very challenging as the global signal of the malignancy that help with a diagnosis, are at best weak.

show abstract

Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs

Cited by 24 publications

References 75 publications

Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status

Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status

Predicting the gist of breast cancer on a screening mammogram using global radiomic features

False-negative diagnosis might occur due to absence of the global radiomic signature of malignancy on screening mammograms

Contact Info

Product

Resources

About