Dr. Peifang Liu Profile

Dr. Peifang Liu

at Tianjin Medical Univ Cancer Institute & Hospital

SPIE Involvement:

Author

Publications (6)

SPIE Journal Paper | 6 June 2022

Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset

Hui Li, Heather M. Whitney, Yu Ji, Alexandra Edwards, John Papaioannou, Peifang Liu, Maryellen Giger

JMI, Vol. 9, Issue 03, 034502, (June 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.3.034502

KEYWORDS: Breast, Magnetic resonance imaging, Artificial intelligence, Diagnostics, Drug discovery, Data modeling, Performance modeling, Breast cancer, Tumor growth modeling, Feature selection

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Purpose: We demonstrate continuous learning and assess its impact on the performance of artificial intelligence of breast dynamic contrast-enhanced magnetic resonance imaging in the task of distinguishing malignant from benign lesions on an independent clinical test dataset. Approach: The study included 1979 patients with 1990 lesions who underwent breast MR imaging during 2015, 2016, and 2017, retrospectively collected under an IRB-approved protocol; there were 1494 malignant and 496 benign lesions based on histopathology. AI was conducted in the task of distinguishing malignant and benign lesions, and independent testing was performed to assess the effect of increasing the numbers of training cases. Five training sets mimicking clinical implementation of continuous AI learning included cases from (1) first quarter of 2015, (2) first half of 2015, (3) all 2015, (4) all 2015 and first half of 2016, and (5) all 2015 and 2016. All classifiers were evaluated on the 2017 independent test set. The area under the ROC curve (AUC) served as the performance metric and was calculated over all lesions in the test set, as well as only mass lesions and only non-mass enhancements. The Mann–Kendall test was used to determine if continuous learning resulted in a positive trend in classification performance. P < 0.05 was considered to be statistically significant. Results: Over the continuous training period, the selected feature subsets tended to become more similar and stable. Performance of the five training conditions on the independent test dataset yielded AUCs of 0.86 (95% CI: [0.83,0.90]), 0.87 (95% CI: [0.83,0.90]), 0.88 (95% CI: [0.84,0.91]), 0.89 (95% CI: [0.85,0.92]), and 0.89 (95% CI: [0.86,0.92]). The Mann–Kendall test indicated a statistically significant positive trend (P = 0.0167) in classification performance with continuous learning. Conclusions: Improved diagnostic performance over time was observed when continuous learning of AI was implemented on an independent clinical test dataset.

Proceedings Article | 4 April 2022 Presentation + Paper

Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers

Heather Whitney, Yu Ji, Hui Li, Peifang Liu, Maryellen Giger

Proceedings Volume 12033, 120330T (2022) https://doi.org/10.1117/12.2612205

KEYWORDS: Breast cancer, Artificial intelligence, Cancer, Medical imaging, Magnetic resonance imaging, Computer aided diagnosis and therapy

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A single breast cancer lesion can have different luminal molecular subtyping when using either immunohistochemical (IHC) staining alone or the St. Gallen criteria that includes Ki-67. This may impact artificial intelligence/computer aided diagnosis (AI/CADx) for determining molecular subtype from medical images. We investigated this using 28 radiomic features extracted from DCE-MR images of 877 unique lesions segmented by a fuzzy c-means method, for three groups of lesions: (1) Luminal A lesions by both reference standards (“agreement”), (2) lesions that were Luminal A by IHC and Luminal B by St. Gallen (“disagreement”), and (3) Luminal B lesions by both reference standards (“agreement”). The Kruskal-Wallis (KW) test for statistically significant differences in groups of lesions was sequentially followed by the Mann-Whitney U test to determine pair-wise statistical difference between groups for relevant features from the KW test. Classification of lesions as Luminal A or Luminal B using all available radiomic features was conducted using three sets of lesions: (1) lesions with IHC alone molecular subtyping, (2) lesions with St. Gallen molecular subtyping, and (3) agreement lesions. Five-fold cross-validation using stepwise feature selection/linear discriminant analysis classifier classified lesions in each set, with performance measured by the area under the receiver operating characteristic curve (AUC). Six features (sphericity, irregularity, surface area to volume ratio, variance of radial gradient histogram, sum average, and volume of most enhancing voxels) were significantly different among the three groups of features with mixed difference of the disagreement group of lesions to the two agreement luminal groups. When using agreement lesions, more features were selected for classification and the AUC was significantly higher (P < 0.003) than using lesions subtyped by either reference standard. The results suggest that the disagreement of reference standards may impact the development of medical imaging AI/CADx methods for determining molecular subtype.

SPIE Journal Paper | 5 March 2020 Open Access

Harmonization of radiomic features of breast lesions across international DCE-MRI datasets

Heather Whitney, Hui Li, Yu Ji, Peifang Liu, Maryellen Giger

JMI, Vol. 7, Issue 01, 012707, (March 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.1.012707

KEYWORDS: Cancer, Breast, Databases, Feature extraction, Computer aided diagnosis and therapy, Medical imaging, Image enhancement, Breast cancer, Solid modeling, Image classification

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Proceedings Article | 13 March 2019 Presentation + Paper

Evaluating deep learning techniques for dynamic contrast-enhanced MRI in the diagnosis of breast cancer

Rachel Anderson, Hui Li, Yu Ji, Peifang Liu, Maryellen Giger

Proceedings Volume 10950, 1095006 (2019) https://doi.org/10.1117/12.2512667

KEYWORDS: Computer aided diagnosis and therapy, Feature extraction, Magnetic resonance imaging, Breast cancer, Medical imaging, Cancer, Breast, Convolutional neural networks, Databases, Image classification

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