Using quantitative image analysis to classify axillary lymph nodes on breast MRI: A new application for the Z 0011 Era

Schacht, David; Drukker, Karen; Pak, Iris; Abé, Hiroyuki; Giger, Maryellen L.

doi:10.1016/j.ejrad.2014.12.003

Cited by 27 publications

(21 citation statements)

References 16 publications

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“…Baltzer et al [ 27 ] investigated only the margin of lymph nodes as a morphological parameter reporting a sensitivity of 41.2% and a specificity of 95.2%. Schacht et al [ 28 ] reported the results of a quantitative breast MR image analysis for classification of axillary lymph nodes. The best features in that study were the circularity as a morphological parameter with AUROC of 0.67 and the wash-out rate with AUROC of 0.62.…”

Section: Discussionmentioning

confidence: 99%

Use of Quantitative Morphological and Functional Features for Assessment of Axillary Lymph Node in Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Fusco

Sansone

Granata

et al. 2018

BioMed Research International

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Background Axillary lymph-node assessment is considered one of the most important prognostic factors concerning breast cancer survival. Objective We investigated the discriminative power of morphological and functional features in assessing the axillary lymph node. Methods We retrospectively analysed data from 52 consecutive patients who undergone DCE-MRI and were diagnosed with primary breast carcinoma: 94 lymph nodes were identified. Per each lymph node, we extracted morphological features: circularity, compactness, convexity, curvature, elongation, diameter, eccentricity, irregularity, radial length, entropy, rectangularity, roughness, smoothness, sphericity, spiculation, surface, and volume. Moreover, we extracted functional features: time to peak (TTP), maximum signal difference (MSD), wash-in intercept (WII), wash-out intercept (WOI), wash-in slope (WIS), wash-out slope (WOS), area under gadolinium curve (AUGC), area under wash-in (AUWI), and area under wash-out (AUWO). Selection of important features in predicting metastasis has been done by means of receiver operating characteristic (ROC) analysis. Performance of linear discriminant analysis was analysed. Results All morphological features but circularity showed a significant difference between median values of metastatic lymph nodes group and nonmetastatic lymph nodes group. All dynamic parameters except for MSD and WOS showed a statistically significant difference between median values of metastatic lymph nodes group and nonmetastatic lymph nodes group. Best results for discrimination of metastatic and nonmetastatic lymph nodes were obtained by AUGC (accuracy 75.8%), WIS (accuracy 71.0%), WOS (accuracy 71.0%), and AUCWO (accuracy 72.6%) for dynamic features and by compactness (accuracy 82.3%), curvature (accuracy 71.0%), radial length (accuracy 71.0%), roughness (accuracy 74.2%), smoothness (accuracy 77.2%), and speculation (accuracy 72.6%) for morphological features. Linear combination of all morphological and/or of all dynamic features did not increase accuracy in metastatic lymph nodes discrimination. Conclusions Compactness as morphological feature and area under time-intensity curve as dynamic feature were the best parameters in identifying metastatic lymph nodes on breast MRI.

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Section: Discussionmentioning

confidence: 99%

Use of Quantitative Morphological and Functional Features for Assessment of Axillary Lymph Node in Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Fusco

Sansone

Granata

et al. 2018

BioMed Research International

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“…Since breast MRI is considered a very useful imaging modality in assessing breast tumor response to neoadjuvant chemotherapy (9, 10), developing and applying breast MRI image feature analysis based on the quantitative image feature analysis or CAD schemes to assess complete response of the breast tumors to the neoadjuvant chemotherapy has been attracting research interest (20, 21). In this paper we reported our latest progress to develop and test new quantitative image feature analysis schemes to assess breast tumor response to the neoadjuvant chemotherapy.…”

Section: Discussionmentioning

confidence: 99%

Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy

Aghaei

Tan

Hollingsworth

et al. 2016

Magnetic Resonance Imaging

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Purpose To develop a new quantitative global kinetic breast MRI features analysis scheme and assess its feasibility to assess tumor response to neoadjuvant chemotherapy. Methods A dataset involving breast MR images acquired from 151 cancer patients before neoadjuvant chemotherapy was used. Among them, 63 patients had complete response (CR) and 88 had partial response (PR) to chemotherapy based on the RECIST criterion. A computer-aided detection (CAD) scheme was applied to segment breast region depicted on the breast MR images and computed a total of 10 kinetic image features to represent parenchyma enhancement either from the entire two breasts or the bilateral asymmetry between the two breasts. To classify between CR and PR cases, we tested an attribution selected classifier that integrates with an artificial neural network and a Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-case-out (LOCO) based cross-validation method. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance. Results From the pool of initial 10 features, 4 features were selected by more than 90% times in the LOCO cross-validation iterations. Among them, 3 represent the bilateral asymmetry of kinetic features between two breasts. Using the classifier yielded AUC = 0.83±0.04, which is significantly higher than using each individual feature to classify between CR and PR cases (p < 0.05). Conclusion This study demonstrated that quantitative analysis of global kinetic features computed from breast MR images acquired pre-chemotherapy has potential to generate a useful clinical marker that is associated with tumor response to neoadjuvant chemotherapy.

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“…These techniques include contrast-enhanced T1w MRI methods, as well as T2w sequences and DWI. 22,31 LN staging is usually based on size of LNs, relative T 2 values, morphology, For the T1-FFE, T2-VISTA and T2-FFE examples, maximum intensity projections of three slices were used to increase signal-to-noise ratio and to cover the same volume as in the T2-TSE and DWI.…”

Section: Discussionmentioning

confidence: 99%

MRI sequences for the detection of individual lymph nodes in regional breast radiotherapy planning

Heijst

Asselen

Pijnappel

et al. 2016

BJR

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Using quantitative image analysis to classify axillary lymph nodes on breast MRI: A new application for the Z 0011 Era

Cited by 27 publications

References 16 publications

Use of Quantitative Morphological and Functional Features for Assessment of Axillary Lymph Node in Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Use of Quantitative Morphological and Functional Features for Assessment of Axillary Lymph Node in Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy

MRI sequences for the detection of individual lymph nodes in regional breast radiotherapy planning

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