Seyedehnafiseh Mirniaharikandehei

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk

This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All mammograms were originally interpreted as negative by radiologists. In the next subsequent mammography screening, 402 women were diagnosed with breast cancer and 642 remained negative. An existing CAD scheme was applied as is to process each image. From CAD-generated results, four detection features including the total number of (1) initial detection seeds and (2) the final detected false-positive regions, (3) average and (4) sum of detection scores, were computed from each image. Then, by combining the features computed from two bilateral images of left and right breasts from either craniocaudal or mediolateral oblique view, two logistic regression models were trained and tested using a leave-one-case-out cross-validation method to predict the likelihood of each testing case being positive in the next subsequent screening. The new prediction model yielded the maximum prediction accuracy with an area under a ROC curve of AUCu2009u2009=u2009u20090.65u2009u2009±u2009u20090.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of (2.95, 6.83). The results also showed an increasing trend in the adjusted odds ratio and risk prediction scores (pu2009u2009<u2009u20090.01). Thus, this study demonstrated that CAD-generated false-positives might include valuable information, which needs to be further explored for identifying and/or developing more effective imaging markers for predicting short-term breast cancer risk.

Developing a unique portable device to non-invasively detect bio-electrochemical characteristics of human tissues

The objective of this study is to develop and test a unique portable device that aims to non-invasively detect bio-electrochemical characteristics of human tissues. For this purpose, we designed and developed a new portable Bio-impedance Spectroscopy (BIS) system utilizing active probe technique as measurement technique for bioelectrical features. This BIS system includes the integrated current source and output voltage signal detection sensors. Active probes are placed on the skin surface of the targeted human organ tissues to directly detect bioimpedance signals. Bio-impedance spectrum was measured by applying electrical currents over a range of frequencies (10kHz − 3MHz). The spectrum was then quantitatively analyzed to produce new biomarkers based on bio-electrochemical characteristics of human tissues. These new bioelectrical markers aim to accurately and reproducibly predict and/or detect human diseases (including cancer). To address the feasibility of this new research technique, we conducted a comprehensive evaluation of new BIS device with its calibration techniques and phantom study. Results showed that the computed bioelectrical marker values monotonically change corresponding to tissue compositions. In this research, we demonstrated how to compute independent and dependent bioelectrical features to be implemented on machine learning (ML) models that can improve our understanding of disease or cancer risk state. The study suggested that using this new device has potential for different applications, including the noninvasive assessment of breast density and the detection of asymmetrical focal areas between two bilateral breasts, which may eventually help more accurately predict breast cancer risk.

Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

Both conventional and deep machine learning has been used to develop decision-support tools applied in medical imaging informatics. In order to take advantages of both conventional and deep learning approach, this study aims to investigate feasibility of applying a locally preserving projection (LPP) based feature regeneration algorithm to build a new machine learning classifier model to predict short-term breast cancer risk. First, a computer-aided image processing scheme was used to segment and quantify breast fibro-glandular tissue volume. Next, initially computed 44 image features related to the bilateral mammographic tissue density asymmetry were extracted. Then, an LLP-based feature combination method was applied to regenerate a new operational feature vector using a maximal variance approach. Last, a k-nearest neighborhood (KNN) algorithm based machine learning classifier using the LPP-generated new feature vectors was developed to predict breast cancer risk. A testing dataset involving negative mammograms acquired from 500 women was used. Among them, 250 were positive and 250 remained negative in the next subsequent mammography screening. Applying to this dataset, LLP-generated feature vector reduced the number of features from 44 to 4. Using a leave-onecase-out validation method, area under ROC curve produced by the KNN classifier significantly increased from 0.62 to 0.68 (p < 0.05) and odds ratio was 4.60 with a 95% confidence interval of [3.16, 6.70]. Study demonstrated that this new LPP-based feature regeneration approach enabled to produce an optimal feature vector and yield improved performance in assisting to predict risk of women having breast cancer detected in the next subsequent mammography screening.

A hybrid deep learning approach to predict malignancy of breast lesions using mammograms

Applying deep learning technology to medical imaging informatics field has been recently attracting extensive research interest. However, the limited medical image dataset size often reduces performance and robustness of the deep learning based computer-aided detection and/or diagnosis (CAD) schemes. In attempt to address this technical challenge, this study aims to develop and evaluate a new hybrid deep learning based CAD approach to predict likelihood of a breast lesion detected on mammogram being malignant. In this approach, a deep Convolutional Neural Network (CNN) was firstly pre-trained using the ImageNet dataset and serve as a feature extractor. A pseudo-color Region of Interest (ROI) method was used to generate ROIs with RGB channels from the mammographic images as the input to the pre-trained deep network. The transferred CNN features from different layers of the CNN were then obtained and a linear support vector machine (SVM) was trained for the prediction task. By applying to a dataset involving 301 suspicious breast lesions and using a leave-one-case-out validation method, the areas under the ROC curves (AUC) = 0.762 and 0.792 using the traditional CAD scheme and the proposed deep learning based CAD scheme, respectively. An ensemble classifier that combines the classification scores generated by the two schemes yielded an improved AUC value of 0.813. The study results demonstrated feasibility and potentially improved performance of applying a new hybrid deep learning approach to develop CAD scheme using a relatively small dataset of medical images.

Association between background parenchymal enhancement of breast MRI and BIRADS rating change in the subsequent screening

Although breast magnetic resonance imaging (MRI) has been used as a breast cancer screening modality for high-risk women, its cancer detection yield remains low (i.e., ≤ 3%). Thus, increasing breast MRI screening efficacy and cancer detection yield is an important clinical issue in breast cancer screening. In this study, we investigated association between the background parenchymal enhancement (BPE) of breast MRI and the change of diagnostic (BIRADS) status in the next subsequent breast MRI screening. A dataset with 65 breast MRI screening cases was retrospectively assembled. All cases were rated BIRADS-2 (benign findings). In the subsequent screening, 4 cases were malignant (BIRADS-6), 48 remained BIRADS-2 and 13 were downgraded to negative (BIRADS-1). A computer-aided detection scheme was applied to process images of the first set of breast MRI screening. Total of 33 features were computed including texture feature and global BPE features. Texture features were computed from either a gray-level co-occurrence matrix or a gray level run length matrix. Ten global BPE features were also initially computed from two breast regions and bilateral difference between the left and right breasts. Box-plot based analysis shows positive association between texture features and BIRADS rating levels in the second screening. Furthermore, a logistic regression model was built using optimal features selected by a CFS based feature selection method. Using a leave-one-case-out based cross-validation method, classification yielded an overall 75% accuracy in predicting the improvement (or downgrade) of diagnostic status (to BIRAD-1) in the subsequent breast MRI screening. This study demonstrated potential of developing a new quantitative imaging marker to predict diagnostic status change in the short-term, which may help eliminate a high fraction of unnecessary repeated breast MRI screenings and increase the cancer detection yield.

Applying a new mammographic imaging marker to predict breast cancer risk

Identifying and developing new mammographic imaging markers to assist prediction of breast cancer risk has been attracting extensive research interest recently. Although mammographic density is considered an important breast cancer risk, its discriminatory power is lower for predicting short-term breast cancer risk, which is a prerequisite to establish a more effective personalized breast cancer screening paradigm. In this study, we presented a new interactive computer-aided detection (CAD) scheme to generate a new quantitative mammographic imaging marker based on the bilateral mammographic tissue density asymmetry to predict risk of cancer detection in the next subsequent mammography screening. An image database involving 1,397 women was retrospectively assembled and tested. Each woman had two digital mammography screenings namely, the “current” and “prior” screenings with a time interval from 365 to 600 days. All “prior” images were originally interpreted negative. In “current” screenings, these cases were divided into 3 groups, which include 402 positive, 643 negative, and 352 biopsy-proved benign cases, respectively. There is no significant difference of BIRADS based mammographic density ratings between 3 case groups (p < 0.6). When applying the CAD-generated imaging marker or risk model to classify between 402 positive and 643 negative cases using “prior” negative mammograms, the area under a ROC curve is 0.70±0.02 and the adjusted odds ratios show an increasing trend from 1.0 to 8.13 to predict the risk of cancer detection in the “current” screening. Study demonstrated that this new imaging marker had potential to yield significantly higher discriminatory power to predict short-term breast cancer risk.

Applying a CAD-generated imaging marker to assess short-term breast cancer risk

Although whether using computer-aided detection (CAD) helps improve radiologists’ performance in reading and interpreting mammograms is controversy due to higher false-positive detection rates, objective of this study is to investigate and test a new hypothesis that CAD-generated false-positives, in particular, the bilateral summation of false-positives, is a potential imaging marker associated with short-term breast cancer risk. An image dataset involving negative screening mammograms acquired from 1,044 women was retrospectively assembled. Each case involves 4 images of craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breasts. In the next subsequent mammography screening, 402 cases were positive for cancer detected and 642 remained negative. A CAD scheme was applied to process all “prior” negative mammograms. Some features from CAD scheme were extracted, which include detection seeds, the total number of false-positive regions, an average of detection scores and the sum of detection scores in CC and MLO view images. Then the features computed from two bilateral images of left and right breasts from either CC or MLO view were combined. In order to predict the likelihood of each testing case being positive in the next subsequent screening, two logistic regression models were trained and tested using a leave-one-case-out based cross-validation method. Data analysis demonstrated the maximum prediction accuracy with an area under a ROC curve of AUC=0.65±0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of [2.95, 6.83]. The results also illustrated an increasing trend in the adjusted odds ratio and risk prediction scores (p<0.01). Thus, the study showed that CAD-generated false-positives might provide a new quantitative imaging marker to help assess short-term breast cancer risk.

Exploring a new bilateral focal density asymmetry based image marker to predict breast cancer risk

Although breast density has been widely considered an important breast cancer risk factor, it is not very effective to predict risk of developing breast cancer in a short-term or harboring cancer in mammograms. Based on our recent studies to build short-term breast cancer risk stratification models based on bilateral mammographic density asymmetry, we in this study explored a new quantitative image marker based on bilateral focal density asymmetry to predict the risk of harboring cancers in mammograms. For this purpose, we assembled a testing dataset involving 100 positive and 100 negative cases. In each of positive case, no any solid masses are visible on mammograms. We developed a computer-aided detection (CAD) scheme to automatically detect focal dense regions depicting on two bilateral mammograms of left and right breasts. CAD selects one focal dense region with the maximum size on each image and computes its asymmetrical ratio. We used this focal density asymmetry as a new imaging marker to divide testing cases into two groups of higher and lower focal density asymmetry. The first group included 70 cases in which 62.9% are positive, while the second group included 130 cases in which 43.1% are positive. The odds ratio is 2.24. As a result, this preliminary study supported the feasibility of applying a new focal density asymmetry based imaging marker to predict the risk of having mammography-occult cancers. The goal is to assist radiologists more effectively and accurately detect early subtle cancers using mammography and/or other adjunctive imaging modalities in the future.

Exploring a new quantitative image marker to assess benefit of chemotherapy to ovarian cancer patients

Accurately assessing the potential benefit of chemotherapy to cancer patients is an important prerequisite to developing precision medicine in cancer treatment. The previous study has shown that total psoas area (TPA) measured on preoperative cross-section CT image might be a good image marker to predict long-term outcome of pancreatic cancer patients after surgery. However, accurate and automated segmentation of TPA from the CT image is difficult due to the fuzzy boundary or connection of TPA to other muscle areas. In this study, we developed a new interactive computer-aided detection (ICAD) scheme aiming to segment TPA from the abdominal CT images more accurately and assess the feasibility of using this new quantitative image marker to predict the benefit of ovarian cancer patients receiving Bevacizumab-based chemotherapy. ICAD scheme was applied to identify a CT image slice of interest, which is located at the level of L3 (vertebral spines). The cross-sections of the right and left TPA are segmented using a set of adaptively adjusted boundary conditions. TPA is then quantitatively measured. In addition, recent studies have investigated that muscle radiation attenuation which reflects fat deposition in the tissue might be a good image feature for predicting the survival rate of cancer patients. The scheme and TPA measurement task were applied to a large national clinical trial database involving 1,247 ovarian cancer patients. By comparing with manual segmentation results, we found that ICAD scheme could yield higher accuracy and consistency for this task. Using a new ICAD scheme can provide clinical researchers a useful tool to more efficiently and accurately extract TPA as well as muscle radiation attenuation as new image makers, and allow them to investigate the discriminatory power of it to predict progression-free survival and/or overall survival of the cancer patients before and after taking chemotherapy.