PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images

Armato, Samuel G.; Huisman, Henkjan; Drukker, Karen; Hadjiiski, Lubomir M.; Kirby, Justin; Petrick, Nicholas; Redmond, George; Giger, Maryellen L.; Cha, Kenny; Mamonov, Artem B.; Kalpathy–Cramer, Jayashree; Farahani, Keyvan

doi:10.1117/1.jmi.5.4.044501

Cited by 113 publications

(115 citation statements)

References 42 publications

(53 reference statements)

Supporting

Mentioning

111

Contrasting

Order By: Relevance

“…Over the 71 computerized methods, the AUC of the testing cohort with 208 lesions ranged from 0.45 to 0.87. 12 Our model achieved an AUC of 0.84 which was in line with other top performing models.…”

Section: Fig 3 Visualization Of Intermediate Feature Mappings For Esupporting

confidence: 84%

A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network

et al. 2020

View full text Add to dashboard Cite

Purpose: Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. In this paper, an efficient convolutional neural network (CNN) was developed and the model interpretation at various convolutional layers was systematically analyzed to improve the understanding of how CNN interprets multimodality medical images and the predictive powers of features at each layer. The problem of small sample size was addressed by feeding the intermediate features into a traditional classification algorithm known as weighted extreme learning machine (wELM), with imbalanced distribution among output categories taken into consideration. Methods: The training data collection used a retrospective set of prostate MR studies, from SPIE-AAPM-NCI PROSTATEx Challenges held in 2017. Three hundred twenty biopsy samples of lesions from 201 prostate cancer patients were diagnosed and identified as clinically significant (malignant) or not significant (benign). All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE) and diffusion-weighted (DW) imaging. After registration and lesion-based normalization, a CNN with four convolutional layers were developed and trained on tenfold cross validation. The features from intermediate layers were then extracted as input to wELM to test the discriminative power of each individual layer. The best performing model from the tenfolds was chosen to be tested on the holdout cohort from two sources. Feature maps after each convolutional layer were then visualized to monitor the trend, as the layer propagated. Scatter plotting was used to visualize the transformation of data distribution. Finally, a class activation map was generated to highlight the region of interest based on the model perspective. Results: Experimental trials indicated that the best input for CNN was a modality combination of T2W, apparent diffusion coefficient (ADC) and DWI b50. The convolutional features from CNN paired with a weighted extreme learning classifier showed substantial performance compared to a CNN end-to-end training model. The feature map visualization reveals similar findings on natural images where lower layers tend to learn lower level features such as edges, intensity changes, etc, while higher layers learn more abstract and task-related concept such as the lesion region. The generated saliency map revealed that the model was able to focus on the region of interest where the lesion resided and filter out background information, including prostate boundary, rectum, etc. Conclusions: This work designs a customized workflow for the small and imbalanced dataset of prostate mpMRI where features w...

show abstract

Section: Fig 3 Visualization Of Intermediate Feature Mappings For Esupporting

confidence: 84%

A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network

et al. 2020

View full text Add to dashboard Cite

show abstract

“…A total of 206 patients from this dataset were scanned at the Radboud University Medical Center (Nijmegen, the Netherlands) in 2012, and these patients comprised the present study population. Patients in the ProstateX dataset had a median PSA level of 13 ng/ml (range 1 to 56 ng/ml) with a median age of 66 (range 48 to 83 years) [24]. The mpMRI protocol was performed on a 3.0-T MRI scanner (MAGNETOM Trio or Skyra, Siemens Healthcare); see Table 1 for a summary of applied sequences (more detailed information can be found in the previously published challenge) [25].…”

Section: Patient Datamentioning

confidence: 99%

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

et al. 2019

Self Cite

View full text Add to dashboard Cite

Objectives To create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa). Methods This study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3-5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets. Results The best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980-0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920-0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution. Conclusions The phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa. Key Points • T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly improve diagnostic performance. • Multivariate feature selection and extreme gradient outperform univariate feature selection and random forest. • The developed radiomics model that extracts multiparametric MRI features with an auto-fixed volume of interest may be a valuable addition to visual assessment in diagnosing clinically significant prostate cancer.

show abstract

“…For instance, XmasNet was developed by Liu et al [33] specifically for the PROSTATEx Challenge 2017 [43], inspired by the Visual Geometry Group (VGG) net [44]. Despite its relative simplicity, it achieved state-of-the-art results, outperforming 69 methods of 33 groups and having the second highest Area Under the Receiver Operating Characteristics Curve (AUROC) on the unseen test set.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…This work considers the MRI dataset provided by the PROSTATEx Challenge 2017 [43] as part of the 2017 SPIE Medical Imaging Symposium [56], organized by the Society of Photographic Instrumentation Engineers (SPIE) and supported by the American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI). The aim of the PROSTATEx Challenge 2017 is to develop a quantitative diagnostic classification method of prostate lesions.…”

Section: Experimental Dataset: the Prostatex17 Datasetmentioning

confidence: 99%

“…All studies include T2w, PDw, DCE, and DWI MRI sequences, acquired using the MAGNETOM Trio and Skyra 3T MRI scanner models from Siemens (Siemens Healthineers, Erlangen, Germany), without employing an endorectal coil. For more details on image acquisition, please refer to [43]. Although DCE imaging conveys relevant functional information, it carries the drawback of needing an external agent, typically via the injection of a Gadolinium-based contrast [32,57].…”

Section: Experimental Dataset: the Prostatex17 Datasetmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

et al. 2020

View full text Add to dashboard Cite

Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN.

show abstract

PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images

Cited by 113 publications

References 42 publications

A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network

A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network

Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer

A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

Contact Info

Product

Resources

About