Non-invasive breast tumour grading using ultrasound frequency-dependent backscatter analysis

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Non-invasive breast tumour grading using ultrasound frequency-dependent backscatter analysis. Hadi Tadayyon June 11, 2012. Locally advanced breast cancer (LABC). Clinical challenge High risk for metastasis Characterized as: > 5 cm long axis Skin/chest wall involvement - PowerPoint PPT Presentation

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Non-invasive breast tumor grading using ultrasound frequency-dependent backscatter analysis

Non-invasive breast tumour grading using ultrasound frequency-dependent backscatter analysisHadi TadayyonJune 11, 2012

1Clinical challengeHigh risk for metastasisCharacterized as:> 5 cm long axisSkin/chest wall involvementLymph node involvementTumour grade a histological feature that is a prognostic indicator and is important for treatment designDetermined from pathological examination of biopsy sampleHigher grade higher degree of malignancy / poorer prognosisOur goal: ultrasonically detect variation in tumour grades

Locally advanced breast cancer (LABC)LABC remains to be a clinical challenge due to high risk of metastasisLABC is characterized by any or all of the following featuresOur goal: ultrasonically detect variation in tumour grades so as to provide an alternative to biopsies In order to determine what kind ultrasonic parameters to use to achieve this, we should look back at how cancers have been characterized using quantitative ultrasound in the past to give us an idea where to start

2Midband fit, slope, and intercept used to differentiate:Prostate cancer from benign tumours1Metastic from non-metastatic lymph nodes2

Acoustic scatterer spacing used to characterize breast lesions as benign or malignant3

Quantitative ultrasound characterization of cancersFeleppa et al., 2004 IEEE Trans UFFC, 43(4), 609-619, (1996)Mamou et al., Ultrasound in Med. & Biol., 37(3), 345357, (2011)Y. Bige et al., Ultrasonics 44 , 211215, (2006)

There are two main spectral analysis methods that have been used in the past to characterize cancers.Involves tranforming time domain echoes into frequency domain and fitting a line to the operating bandwidth and extracting the slope, intercept, and midband fit.Used to differentiate prostate cancer from benign prostate hyperplasiaAnd to metastiatic lymph notes from non-metastatic ones

Involves computing the AR estimate of the power spectrum, which gives info about the periodicity of the backscatter, then taking the autocorrelation, can extract the scatterer spacing corresponding to the location of the peak of the SACUsed to characterize breast lesions, but originally developed by Fellingham et al, to charcterize chronic liver disease

3Can LABC tumours be characterized in terms of grade using quantitative ultrasound?Given:Retrospective in-vivo clinical breast data (N=43)A diagnostic ultrasound machineResearch Question4Methods: data collection and classificationTumour ROI1 cmNormal breast ROIQUSMidband fit (MBF)Slope (SS)Intercept (SI)Scatterer spacing (SAS)GI (N=3)GII (N = 22)GIII(N = 18)

10 MHz fc linear array transducer (Ultrasonix, Canada)4-7 cm depth5 MHz 50% bandwidth

Normal tissue ROIUnder guidance of an oncologist, we scanned the affected breast so as to include several slices of the tumor and a slice of normal tissue Use 5mm sliding windows to estimate local QUS paramters we classify the mean QUS parameter as G I, II, or III based on the tumor pathology of mastectomy sample.

5Methods: Spectral analysisDepth-dependent spectral normalization (reference phantom)

Variable bandwidth linear regression

Discrete depth spectral normalization (reference reflector)

Auto-regressive (AR) spectral estimation and autocorrelation-derived scatterer spacing

Rather than having a continuous depth like the phantom, we acquired reference spectra at a discrete number of axial positions by moving the reflector axially wrt the transducer and interpolate the depth spectra for each sample ROI.

Should I explain what is normalization?

6Results: QUS distributions among tumour grades

Mann-Whitney test: p = 0.032All parameters are relative to normal tissue (to minimize demographic variability)

7Results: parametric images of scatterer spacingGIGIIGIIINT

00.5SAS (mm)10 um1 cm- Beside just looking at mean SAS from a population of tumours, we also like to know how SAS can spatially characterize tumours- All pixels of normal tissue is assigned a constant value Scatterer spacing inside tumor is allowed to vary can see dome defferences in the heteregneity of the tumours that you cant see in B-mode histology showsisolated islands of glands of uniform size in gr ILarger islands, mildly packed highly packed, stroma almost completely disapeared

8The link of scatterer spacing to biology

Mean spacing between glandular islands = 200 um 100 umA potential method to non-invasively characterize tumour grade was proposedScatterer spacing statistically different among tumour grades (ANOVA test & Mann-Whitney test)Scatterer spacing is linked to spacing between glandular islands Small sample size for GIIn large population study 362/1409 = 25%In our study, 3/43 = 7%Cannot evaluate classification due to insufficient parametersFuture directions: investigate other QUS parameters

Discussion & conclusionOne of the limitations of the study was the small sample size of grade ILarge population study had 362/1409 grade I tumoursBy increasing population size, we hope to increase our grade I tumours

Future: other QUS parameters envelope statics10AcknowledgmentsCzarnota Lab, University of TorontoDr. CzarnotaDr. Omar FalouMike PapanicolauSara IradjiErvis Sofroni

Ryerson UniversityDr. Lauren Wirtzfeld

University of IllinoisDr. Michael Oelze

CGSD14:37 (June 7)11:35 (June 8)14:3011Tumour gradeIncreasing risk of metastasis

Grade IGrade IIGrade IIITotal score 3-5 6-78-9