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Texture analysis on MR images helps predicting non-response to NAC in breast cancer.

Michoux N, Van den Broeck S, Lacoste L, Fellah L, Galant C, Berlière M, Leconte I - BMC Cancer (2015)

Bottom Line: A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity.Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC.Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.

View Article: PubMed Central - PubMed

Affiliation: Radiology Department, IREC (Institute of Experimental and Clinical Research) - IMAG, Université Catholique de Louvain, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, Brussels, B1200, Belgium. nicolas.michoux@uclouvain.be.

ABSTRACT

Background: To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI.

Methods: Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC.

Results: A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction.

Conclusion: Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.

No MeSH data available.


Related in: MedlinePlus

Pixel-level analysis of breast MRI texture in a CR patient with a mass enhancement. Are respectively displayed, a the axial subtracted image and the maps based on b contrast, c correlation, d difference variance, e energy, f entropy, g inverse differential moment (which is correlated with the homogeneity parameter), h sum average and i sum variance from the GLCM, with mean value estimated on a 3x3 neighbourhood around the pixel of interest then normalized on the 0–255 range. Individual texture parameters reveal different local and regional statistical properties of the grey level intensity between (and respectively within) breast lesions and normal parenchyma. Combination of all or parts of the texture parameters helps classifying patients according to their response to NAC
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Fig4: Pixel-level analysis of breast MRI texture in a CR patient with a mass enhancement. Are respectively displayed, a the axial subtracted image and the maps based on b contrast, c correlation, d difference variance, e energy, f entropy, g inverse differential moment (which is correlated with the homogeneity parameter), h sum average and i sum variance from the GLCM, with mean value estimated on a 3x3 neighbourhood around the pixel of interest then normalized on the 0–255 range. Individual texture parameters reveal different local and regional statistical properties of the grey level intensity between (and respectively within) breast lesions and normal parenchyma. Combination of all or parts of the texture parameters helps classifying patients according to their response to NAC

Mentions: The visual texture of breast tissues was assessed from the grey level co-occurrence matrix (GLCM) and the run length matrix (RLM) [29, 40]. From the GLCM, nine textural features describing the grey levels interdependence in the image were estimated (Fig. 4). Computation parameters were: distance of one pixel between two neighbouring pixels, average of the angular relationships on the four main directions, five bits of grey levels. From the RLM, eleven textural features describing the distribution of runs of grey levels in the image were estimated with the same computation parameters. The mean value (over all pixels in the ROI studied) of the textural features was estimated. The list of studied parameters is given in Table 3.Fig. 4


Texture analysis on MR images helps predicting non-response to NAC in breast cancer.

Michoux N, Van den Broeck S, Lacoste L, Fellah L, Galant C, Berlière M, Leconte I - BMC Cancer (2015)

Pixel-level analysis of breast MRI texture in a CR patient with a mass enhancement. Are respectively displayed, a the axial subtracted image and the maps based on b contrast, c correlation, d difference variance, e energy, f entropy, g inverse differential moment (which is correlated with the homogeneity parameter), h sum average and i sum variance from the GLCM, with mean value estimated on a 3x3 neighbourhood around the pixel of interest then normalized on the 0–255 range. Individual texture parameters reveal different local and regional statistical properties of the grey level intensity between (and respectively within) breast lesions and normal parenchyma. Combination of all or parts of the texture parameters helps classifying patients according to their response to NAC
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526309&req=5

Fig4: Pixel-level analysis of breast MRI texture in a CR patient with a mass enhancement. Are respectively displayed, a the axial subtracted image and the maps based on b contrast, c correlation, d difference variance, e energy, f entropy, g inverse differential moment (which is correlated with the homogeneity parameter), h sum average and i sum variance from the GLCM, with mean value estimated on a 3x3 neighbourhood around the pixel of interest then normalized on the 0–255 range. Individual texture parameters reveal different local and regional statistical properties of the grey level intensity between (and respectively within) breast lesions and normal parenchyma. Combination of all or parts of the texture parameters helps classifying patients according to their response to NAC
Mentions: The visual texture of breast tissues was assessed from the grey level co-occurrence matrix (GLCM) and the run length matrix (RLM) [29, 40]. From the GLCM, nine textural features describing the grey levels interdependence in the image were estimated (Fig. 4). Computation parameters were: distance of one pixel between two neighbouring pixels, average of the angular relationships on the four main directions, five bits of grey levels. From the RLM, eleven textural features describing the distribution of runs of grey levels in the image were estimated with the same computation parameters. The mean value (over all pixels in the ROI studied) of the textural features was estimated. The list of studied parameters is given in Table 3.Fig. 4

Bottom Line: A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity.Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC.Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.

View Article: PubMed Central - PubMed

Affiliation: Radiology Department, IREC (Institute of Experimental and Clinical Research) - IMAG, Université Catholique de Louvain, Cliniques Universitaires Saint-Luc, Avenue Hippocrate 10, Brussels, B1200, Belgium. nicolas.michoux@uclouvain.be.

ABSTRACT

Background: To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI.

Methods: Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC.

Results: A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction.

Conclusion: Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.

No MeSH data available.


Related in: MedlinePlus