Association of distant recurrence‐free survival with algorithmically extracted MRI characteristics in breast cancer

Mazurowski, Maciej A.; Saha, Ashirbani; Harowicz, Michael R.; Cain, Elizabeth Hope; Marks, Jeffrey R.; Marcom, P. Kelly

doi:10.1002/jmri.26648

Cited by 22 publications

(22 citation statements)

References 40 publications

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“…Kurtosis, selected from one of the first-order features, was negatively associated with the risk of disease in this study. Many recent studies have particularly emphasized the significance of Kurtosis in colorectal, pancreatic, and breast cancers ( 16 , 32 , 33 ). The large area emphasis belongs to one of the features of GLSZM and is very suitable for quantifying the texture and heterogeneity of tumors because it considers the interaction between adjacent pixels ( 34 , 35 ).…”

Section: Discussionmentioning

confidence: 99%

The Impact of Preoperative Radiomics Signature on the Survival of Breast Cancer Patients With Residual Tumors After NAC

et al. 2021

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BackgroundResidual cancer cells remaining after chemotherapy may have more aggressive behavior that promotes recurrence or metastasis, and which patients would benefit from subsequent additional treatment is controversial. The purpose of our study was to evaluate the prognostic value of the preoperative radiomics features of computed tomography (CT) imaging in breast cancer (BC) patients with residual tumors after neoadjuvant chemotherapy (NAC).MethodsPost-NAC CT images were reviewed from 114 patients who had received breast surgery and had residual breast tumors. The association of the 110 radiomics features derived from CT images with 5-year disease-free survival (DFS) was assessed by log-rank test in the training cohort, resulting in 13 prognostic radiomics features.ResultsWe constructed a radiomics signature consisting of four selected features by using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, which performed well in the discrimination with an area under the curve (AUC) of 0.78 (95% CI, 0.67–0.89) and 0.73 (95% CI, 0.59–0.87) in the training and validation cohorts, respectively. Radiomics nomogram, incorporating the radiomics signature with the conventional clinical variables, also performed well in the two cohorts (training cohort: AUC, 0.84; validation cohort: AUC, 0.82). Moreover, we found that the high-risk patients determined by our radiomics nomogram could benefit from postoperative adjuvant chemotherapy, while the low-risk and total patient groups could not.ConclusionsOur novel radiomics nomogram is a promising and favorable prognostic biomarker for preoperatively predicting survival outcomes and may aid in clinical decision-making in BC patients with residual tumors after NAC.

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Section: Discussionmentioning

confidence: 99%

The Impact of Preoperative Radiomics Signature on the Survival of Breast Cancer Patients With Residual Tumors After NAC

et al. 2021

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“…MajorAxisLength was found to associate with distant recurrence-free survival in Breast Cancer. 37 Inertia was significantly correlated with fibroblast growth factor receptor (FGFR) in head and neck squamous cell carcinoma. 38 A study exploring wavelet-based short-run high gray-level emphasis found that the short-run high gray-level emphasis achieved high accuracy in the classification between benign and malignant masses, suggesting that radiomics features extracted from MRI are highly correlated with ovarian epithelial cancer classification and prognosis.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of Noninvasive MRI‐Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma

Zhao

Zhang²,

Zhu³

et al. 2021

Magnetic Resonance Imaging

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Background Cholangiocarcinoma is a type of hepatobiliary tumor. For perihilar cholangiocarcinoma (pCCA), patients who experience early recurrence (ER) have a poor prognosis. Preoperative accurate prediction of postoperative ER can avoid unnecessary operation; however, prediction is challenging. Purpose To develop a novel signature based on clinical and/or MRI radiomics features of pCCA to preoperatively predict ER. Study Type Retrospective. Population One hundred eighty‐four patients (median age, 61.0 years; interquartile range: 53.0–66.8 years) including 115 men and 69 women. Field Strength/Sequence A 1.5 T; volumetric interpolated breath‐hold examination (VIBE) sequence. Assessment The models were developed from the training set (128 patients) and validated in a separate testing set (56 patients). The contrast‐enhanced arterial and portal vein phase MR images of hepatobiliary system were used for extracting radiomics features. The correlation analysis, least absolute shrinkage and selection operator (LASSO) logistic regression (LR), backward stepwise LR were mainly used for radiomics feature selection and modeling (Modelradiomic). The univariate and multivariate backward stepwise LR were used for preoperative clinical predictors selection and modeling (Modelclinic). The radiomics and preoperative clinical predictors were combined by multivariate LR method to construct clinic‐radiomics nomogram (Modelcombine). Statistical Tests Chi‐squared (χ2) test or Fisher's exact test, Mann–Whitney U‐test or t‐test, Delong test. Two tailed P < 0.05 was considered statistically significant. Results Based on the comparison of area under the curves (AUC) using Delong test, Modelclinic and Modelcombine had significantly better performance than Modelradiomic and tumor‐node‐metastasis (TNM) system in training set. In the testing set, both Modelclinic and Modelcombine had significantly better performance than TNM system, whereas only Modelcombine was significantly superior to Modelradiomic. However, the AUC values were not significantly different between Modelclinic and Modelcombine (P = 0.156 for training set and P = 0.439 for testing set). Data Conclusion A noninvasive model combining the MRI‐based radiomics signature and clinical variables is potential to preoperatively predict ER for pCCA. Level of Evidence 3 Technical Efficacy Stage 4

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“…Li et al [20] also found that tumors with a high risk of recurrence, based on ODX results, correlated with the enhancement texture, according to the maximum enhancing voxels within a lesion. Mazurowski et al [41] investigated the association of distant recurrence-free survival with MRI characteristics in breast cancer, and found the strongest associations with distant recurrence-free survival were signal enhancement ratio partial tumor volume, kurtosis of the signal enhancement ratio map within tumor and the other quantitative data. Assessment of heterogeneity, including skewness and kurtosis, may be important for deciding treatment options for breast cancer patients and for predicting recurrence prognosis.…”

Section: Discussionmentioning

confidence: 99%

Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer

et al. 2020

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To examine the correlation of qualitative and quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) results with 95-gene classifier or Curebest TM 95-gene classifier Breast (95GC) results for recurrence prediction in estrogen receptorpositive breast cancer (ERPBC).This retrospective study included 78 ERPBC patients (age range, 24-74 years) classified into high-(n = 33) and low-(n = 45) risk groups for recurrence based on 95GC and who underwent DCE-MRI between July 2006 and November 2012. For qualitative evaluation, mass shape, margin, and internal enhancement based on BI-RADS MRI lexicon and multiplicity were determined by consensus interpretation by 2 breast radiologists. For quantitative evaluation, mass size, volume ratios of the DCE-MRI kinetics, and both the kurtosis and the skewness of the intensity histogram for the whole mass in the initial and delayed phases were determined. Differences between the 2 risk-groups were analyzed using univariate logistic regression analyses and multiple logistic regression analyses. Receiver-operating characteristic curve cut-off values were used to define the groups.As for the qualitative findings, the difference between the 2 groups was not significant. For the quantitative data, the volume ratio of "medium" in the initial phase differed significantly between the 2 groups (P = .049). The volume ratio of "medium" (P = .006) and of "slow-persistent" (P = .005), and the delayed phase kurtosis (P = .012) in the univariate logistic regression analyses, and in the multiple logistic regression, volume ratio of "medium" >38.9% and delayed phase kurtosis >3.31 were identified as significant high-risk indicators (odds ratio, 5.83 and 3.55; 95% confidence interval, 1.58 to 21.42 and 1.24 to 10.15; P = .008 and P = .018, respectively).A high volume ratio of "medium" in the initial phase and/or high kurtosis in the delayed phase for quantitative evaluation could predict high ERPBC recurrence risk based on 95GC. Abbreviations: 95GC = 95-gene classifier or Curebest TM 95-gene classifier breast, DCE-MRI = dynamic contrast-enhanced magnetic resonance imaging, ER = estrogen receptor, ERPBC = estrogen receptor-positive breast cancer, GC = gene classifier, MLR = multiple logistic regression analyses, NAC = neoadjuvant chemotherapy, ODX = Oncotype DX, RS = recurrence score, ULR = univariate logistic regression analyses.

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Association of distant recurrence‐free survival with algorithmically extracted MRI characteristics in breast cancer

Cited by 22 publications

References 40 publications

The Impact of Preoperative Radiomics Signature on the Survival of Breast Cancer Patients With Residual Tumors After NAC

The Impact of Preoperative Radiomics Signature on the Survival of Breast Cancer Patients With Residual Tumors After NAC

Development and Validation of Noninvasive MRI‐Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma

Radiogenomics of magnetic resonance imaging and a new multi-gene classifier for predicting recurrence prognosis in estrogen receptor-positive breast cancer

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