Literature DB >> 29694675

Recurrence quantification analysis of radiologists' scanpaths when interpreting mammograms.

Ziba Gandomkar1, Kevin Tay2, Patrick C Brennan1, Claudia Mello-Thoms1,3.   

Abstract

PURPOSE: The purpose of this study was to Propose a classifier based on recurrence quantification analysis (RQA) metrics for distinguishing experts' scanpaths from those of less-experienced readers and to explore the association of spatiotemporal dynamics of the mammographic scanpaths with the characteristics of cases and radiologists using RQA metrics.
MATERIALS AND METHODS: Eye movements were recorded from eight radiologists (two cohorts: four experienced and four less-experienced) while reading 120 mammograms (59 cancer, 61 normal). Ten RQA measures were extracted for each recorded scanpath. The measures described the temporal distribution of recurrent fixations as well as laminar and deterministic eye movements. Recurrent fixations are fixations that are located close to a previously fixated point in a scanpath. Deterministic eye movements represent looking back and forth between two locations, while laminar eye movements indicate detailed scanning of an area with consecutive fixations. The RQA metrics along with six conventional eye-tracking parameters were used to construct a classifier for distinguishing experts' scanpaths from those of less-experienced readers. Leave-one-out cross validation was used for evaluating the classifier. For each reader cohort, the ANOVA analysis was done to study the relationship of RQA measures with breast density, case pathology, readers' expertise, and readers' decisions on the case. The proportions of laminar and deterministic movements involved fixations in the location of lesions were also compared for two reader cohorts using two proportion z-tests.
RESULTS: All RQA measures differed significantly between scanpaths of experienced readers and those of less-experienced readers. The classifier achieved an area under the receiver operating characteristic curve of 0.89 (0.87-0.91) for detecting experts' scanpaths. Proportionately more refixations and laminar and deterministic sequences were in the location of the lesion for the experienced cohort compared to the less-experienced cohort (all P-values < 0.001). Eight and four RQA measures differed between normal and cancer cases for the experienced and less experienced readers, respectively. None of metrics differed between fatty and dense breasts for the less experienced readers, while two measures resulted into a significant difference for the experienced readers. For experts, six measures differed significantly between true negatives and false positives and nine were significantly different between true positives and false negatives. For the less-experienced cohort, the corresponding figures were seven and one measures, respectively.
CONCLUSION: The RQA measures can quantify the differences among experienced and less experienced radiologists. They also capture differences among experts' scanpaths related to case pathology and radiologists' decisions on the case.
© 2018 American Association of Physicists in Medicine.

Entities:  

Keywords:  breast cancer; eye movements; mammogram; recurrence quantification analysis; scanpaths

Mesh:

Year:  2018        PMID: 29694675     DOI: 10.1002/mp.12935

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  6 in total

1.  Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study.

Authors:  Ziba Gandomkar; Patrick C Brennan; Claudia Mello-Thoms
Journal:  J Digit Imaging       Date:  2019-10       Impact factor: 4.056

2.  Visual search in breast imaging.

Authors:  Ziba Gandomkar; Claudia Mello-Thoms
Journal:  Br J Radiol       Date:  2019-07-18       Impact factor: 3.039

3.  From eye movements to scanpath networks: A method for studying individual differences in expository text reading.

Authors:  Xiaochuan Ma; Yikang Liu; Roy Clariana; Chanyuan Gu; Ping Li
Journal:  Behav Res Methods       Date:  2022-04-20

4.  What do experts look at and what do experts find when reading mammograms?

Authors:  Jeremy M Wolfe; Chia-Chien Wu; Jonathan Li; Sneha B Suresh
Journal:  J Med Imaging (Bellingham)       Date:  2021-07-13

5.  Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspection.

Authors:  Ziba Gandomkar; Somphone Siviengphanom; Ernest U Ekpo; Mo'ayyad Suleiman; Seyedamir Tavakoli Taba; Tong Li; Dong Xu; Karla K Evans; Sarah J Lewis; Jeremy M Wolfe; Patrick C Brennan
Journal:  Sci Rep       Date:  2021-10-11       Impact factor: 4.379

6.  SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves.

Authors:  Robert Ahadizad Newport; Carlo Russo; Sidong Liu; Abdulla Al Suman; Antonio Di Ieva
Journal:  Sensors (Basel)       Date:  2022-09-30       Impact factor: 3.847

  6 in total

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