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Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models.

Khalvati F, Wong A, Haider MA - BMC Med Imaging (2015)

Bottom Line: A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models.We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models.The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. farzad.khalvati@sri.utoronto.ca.

ABSTRACT

Background: Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data.

Methods: In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models.

Results: The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy.

Conclusions: Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a comprehensive set of texture features and a feature selection method, optimal texture feature models were constructed that improved the prostate cancer auto-detection significantly compared to conventional MP-MRI texture feature models.

No MeSH data available.


Related in: MedlinePlus

a T2w does not clearly show a tumour although there is mild signal alteration in the left peripheral zone (arrow). b ADC does not clearly show a tumour (arrow). c CHB-DWI of 2000 s/mm2shows no tumour (arrow). d CDI clearly shows a bright nodule (arrow) corresponding to tumour
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Fig5: a T2w does not clearly show a tumour although there is mild signal alteration in the left peripheral zone (arrow). b ADC does not clearly show a tumour (arrow). c CHB-DWI of 2000 s/mm2shows no tumour (arrow). d CDI clearly shows a bright nodule (arrow) corresponding to tumour

Mentions: Figure 5 shows an example for all four modalities which include T2w, ADC, CHB-DWI, and CDI. As it can be seen, CDI (Fig. 5d) is the only modality that clearly shows a bright nodule where a tumour is located (confirmed by histopathology data - Fig. 6).Fig. 5


Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models.

Khalvati F, Wong A, Haider MA - BMC Med Imaging (2015)

a T2w does not clearly show a tumour although there is mild signal alteration in the left peripheral zone (arrow). b ADC does not clearly show a tumour (arrow). c CHB-DWI of 2000 s/mm2shows no tumour (arrow). d CDI clearly shows a bright nodule (arrow) corresponding to tumour
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4524105&req=5

Fig5: a T2w does not clearly show a tumour although there is mild signal alteration in the left peripheral zone (arrow). b ADC does not clearly show a tumour (arrow). c CHB-DWI of 2000 s/mm2shows no tumour (arrow). d CDI clearly shows a bright nodule (arrow) corresponding to tumour
Mentions: Figure 5 shows an example for all four modalities which include T2w, ADC, CHB-DWI, and CDI. As it can be seen, CDI (Fig. 5d) is the only modality that clearly shows a bright nodule where a tumour is located (confirmed by histopathology data - Fig. 6).Fig. 5

Bottom Line: A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models.We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models.The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. farzad.khalvati@sri.utoronto.ca.

ABSTRACT

Background: Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data.

Methods: In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models.

Results: The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy.

Conclusions: Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a comprehensive set of texture features and a feature selection method, optimal texture feature models were constructed that improved the prostate cancer auto-detection significantly compared to conventional MP-MRI texture feature models.

No MeSH data available.


Related in: MedlinePlus