Breast Cancer Radiogenomics: Current Status and Future Directions

Grimm, Lars J.; Mazurowski, Maciej A.

doi:10.1016/j.acra.2019.09.012

Cited by 49 publications

(36 citation statements)

References 72 publications

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“…Radiogenomics is a novel approach that can correlate imaging characteristics with underlying genes, mutations and expression patterns at the genetic level [ 18 ]. Radiogenomics can be imaging surrogates for genetic tests and can reflect tumour biology [ 19 ]. One recent study demonstrated that radiogenomics of breast cancer could infer underlying gene expression by using RNA sequencing [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Zhou

Liu

et al. 2020

BMC Cancer

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Background The clinicopathological classification of breast cancer is proposed according to therapeutic purposes. It is simplified and can be conducted easily in clinical practice, and this subtyping undoubtedly contributes to the treatment selection of breast cancer. This study aims to investigate the feasibility of using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI for predicting the clinicopathological subtypes of breast cancer. Methods Patients who underwent breast magnetic resonance imaging were confirmed by retrieving data from our institutional picture archiving and communication system (PACS) between March 2013 and September 2017. Five clinicopathological subtypes were determined based on the status of ER, PR, HER2 and Ki-67 from the immunohistochemical test. The radiomic features of diffusion-weighted imaging were derived from the volume of interest (VOI) of each tumour. Fisher discriminant analysis was performed for clinicopathological subtyping by using a backward selection method. To evaluate the diagnostic performance of the radiomic features, ROC analyses were performed to differentiate between immunohistochemical biomarker-positive and -negative groups. Results A total of 84 radiomic features of four statistical methods were included after preprocessing. The overall accuracy for predicting the clinicopathological subtypes was 96.4% by Fisher discriminant analysis, and the weighted accuracy was 96.6%. For predicting diverse clinicopathological subtypes, the prediction accuracies ranged from 92 to 100%. According to the cross-validation, the overall accuracy of the model was 82.1%, and the accuracies of the model for predicting the luminal A, luminal BHER2-, luminal BHER2+, HER2 positive and triple negative subtypes were 79, 77, 88, 92 and 73%, respectively. According to the ROC analysis, the radiomic features had excellent performance in differentiating between different statuses of ER, PR, HER2 and Ki-67. Conclusions The Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI is a reliable method for the prediction of clinicopathological breast cancer subtypes.

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Section: Introductionmentioning

confidence: 99%

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Zhou

Liu

et al. 2020

BMC Cancer

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“…Önceki çalışmalar, mamografik görünümün moleküler alt tip ile korele olabileceğini ve meme tümörünün biyolojik davranışını anlamak için yeni bir bakış açısı sağlayabileceğini göstermektedir. Radyolojik olarak luminal subtiplerin görünüşleri diğer subtiplerden değişkenlik gösterebilir [17][18] . Spikülasyon çevredeki parankimde desmoplastik veya fibrotik reaksiyona bağlı olarak düşük ve orta dereceli tümörlerde görülür.…”

Section: Discussionunclassified

Correlation between Histopathological Molecular Markers and Magnetic Resonance Imaging of Spiculated Breast Cancers

Gökalp

2020

Uludağ Üniversitesi Tıp Fakültesi Dergisi

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Bu çalışmanın amacı spiküle ve spiküle olmayan meme kanserinin MRG ve histopatolojik bulguları arasındaki ilişkiyi karşılaştırmaktır. Ocak 2014 ile Ocak 2018 arasında, mamografide BI-RADS kriterlerine göre 50 spiküle ve 40 spiküle olmayan kitle olarak ultrasonografi kılavuzluğunda biyopsi veya lumpektomi/mastektomi yapılan 90 kadın çalışmaya alındı. Meme kanserinin moleküler biyobelirteçlerini tanımlamak için östrojen reseptörü (ÖR), progesteron reseptörü (PR), HER2 ekspresyonu ve Ki67 indeksi kullanıldı. Korelasyonların istatistiksel önemini ölçmek için Pearson ki-kare testi yapıldı. İki grup arasında yaş açısından fark yoktu (p=0.331). Kitlelerin büyüklüğü iki grup arasında farklı değildi (p=0.244). Spiküle kitlelerde T2A görüntülerde (T2AG) daha fazla hipointens sinyal özelliği tespit edildi (p=0.004). MRG'de multifokal veya multisentrik tutulum, kitlesiz boyanma, periferik halkasal boyanma ve aksiller lenf nodu tutulumu açısından iki grup arasında fark yoktu (sırasıyla p=0.237, p=0.622, p=0.096, p=0.295 ve p=0.764). ÖR ve PR pozitifliği spiküle kitlelerde daha yüksekti (sırasıyla p=0.027 ve p=0.03). HER2 pozitifliği ve Ki67 indeksi için iki grup arasında istatistiksel olarak anlamlı bir fark bulunmadı (sırasıyla p=0.571 ve p=0.596).ÖR ve PR pozitifliği spiküle kitlelerde daha fazla olma eğilimindedir. Bu, hastalığın seyrini ve tedavinin etkinliğini tahmin etmede yardımcı olabilir.

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“…Radiogenomics is a novel approach that can correlate imaging characteristics with underlying genes, mutations and expression patterns at the genetic level [18]. Radiogenomics can be imaging surrogates for genetic tests and can re ect tumour biology [19]. One recent study demonstrated that radiogenomics of breast cancer could infer underlying gene expression by using RNA sequencing [20].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the clinicopathological subtypes of breast cancer using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Zhou

Liu

et al. 2020

Preprint

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Background: The clinicopathological classification of breast cancer is proposed according to therapeutic purposes. It is simplified and can be conducted easily in clinical practice, and this subtyping undoubtedly contributes to the treatment selection of breast cancer. This study aims to investigate the feasibility of using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI for predicting the clinicopathological subtypes of breast cancer. Methods: Patients who underwent breast magnetic resonance imaging were confirmed by retrieving data from our institutional picture archiving and communication system (PACS) between March 2013 and September 2017. Five clinicopathological subtypes were determined based on the status of ER, PR, HER2 and Ki-67 from the immunohistochemical test. The radiomic features of diffusion-weighted imaging were derived from the volume of interest (VOI) of each tumour. Fisher discriminant analysis was performed for clinicopathological subtyping by using a backward selection method. To evaluate the diagnostic performance of the radiomic features, ROC analyses were performed to differentiate between immunohistochemical biomarker-positive and -negative groups. Results: A total of 84 radiomic features of four statistical methods were included after preprocessing. The overall accuracy for predicting the clinicopathological subtypes was 96.4% by Fisher discriminant analysis, and the weighted accuracy was 96.6%. For predicting diverse clinicopathological subtypes, the prediction accuracies ranged from 92% to 100%. According to the cross-validation, the overall accuracy of the model was 82.1%, and the accuracies of the model for predicting the luminal A, luminal BHER2-, luminal BHER2+, HER2 positive and triple negative subtypes were 79%, 77%, 88%, 92% and 73%, respectively. According to the ROC analysis, the radiomic features had excellent performance in differentiating between different statuses of ER, PR, HER2 and Ki-67. Conclusions: The Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI is a reliable method for the prediction of clinicopathological breast cancer subtypes.

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Breast Cancer Radiogenomics: Current Status and Future Directions

Cited by 49 publications

References 72 publications

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Correlation between Histopathological Molecular Markers and Magnetic Resonance Imaging of Spiculated Breast Cancers

Prediction of the clinicopathological subtypes of breast cancer using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

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