Urinary bladder cancer staging in CT urography using machine learning

Purpose: To evaluate the feasibility of using an objective computer‐aided system to assess bladder cancer stage in CT Urography (CTU). Materials and methods: A dataset consisting of 84 bladder cancer lesions from 76 CTU cases was used to develop the computerized system for bladder cancer staging based on machine learning approaches. The cases were grouped into two classes based on pathological stage ≥ T2 or below T2, which is the decision threshold for neoadjuvant chemotherapy treatment clinically. There were 43 cancers below stage T2 and 41 cancers at stage T2 or above. All 84 lesions were automatically segmented using our previously developed auto‐initialized cascaded level sets (AI‐CALS) method. Morphological and texture features were extracted. The features were divided into subspaces of morphological features only, texture features only, and a combined set of both morphological and texture features. The dataset was split into Set 1 and Set 2 for two‐fold cross‐validation. Stepwise feature selection was used to select the most effective features. A linear discriminant analysis (LDA), a neural network (NN), a support vector machine (SVM), and a random forest (RAF) classifier were used to combine the features into a single score. The classification accuracy of the four classifiers was compared using the area under the receiver operating characteristic (ROC) curve (Az). Results: Based on the texture features only, the LDA classifier achieved a test Az of 0.91 on Set 1 and a test Az of 0.88 on Set 2. The test Az of the NN classifier for Set 1 and Set 2 were 0.89 and 0.92, respectively. The SVM classifier achieved test Az of 0.91 on Set 1 and test Az of 0.89 on Set 2. The test Az of the RAF classifier for Set 1 and Set 2 was 0.89 and 0.97, respectively. The morphological features alone, the texture features alone, and the combined feature set achieved comparable classification performance. Conclusion: The predictive model developed in this study shows promise as a classification tool for stratifying bladder cancer into two staging categories: greater than or equal to stage T2 and below stage T2.

[1]  J. Huguet,et al.  Cystectomy in patients with high risk superficial bladder tumors who fail intravesical BCG therapy: pre-cystectomy prostate involvement as a prognostic factor. , 2005, European urology.

[2]  Ronald M. Summers,et al.  Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .

[3]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  N. Petrick,et al.  Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis. , 1998, Medical physics.

[5]  P. Jaccard.  THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[6]  Belur V. Dasarathy,et al.  Image characterizations based on joint gray level-run length distributions , 1991, Pattern Recognit. Lett..

[7]  J. Robbins,et al.  Contrast media , 1999, Springer Berlin Heidelberg.

[8]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[9]  H P Chan,et al.  Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses. , 2001, Medical physics.

[10]  John D Seigne,et al.  Diagnosis and Treatment of Non-Muscle Invasive Bladder Cancer: AUA/SUO Guideline. , 2016, The Journal of urology.

[11]  P Dawson-Edwards,et al.  Cis-diamminedichloroplatinum (DDP) as initial treatment of invasive bladder cancer. , 1984, British journal of urology.

[12]  Joaquim Bellmunt,et al.  A systematic review of neoadjuvant and adjuvant chemotherapy for muscle-invasive bladder cancer. , 2012, European urology.

[13]  C. Metz.  ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[14]  Lubomir M. Hadjiiski,et al.  Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. , 2006, Medical physics.

[15]  Andreas Böhle,et al.  Guidelines on Non-muscle-invasive Bladder Cancer (Ta, T1 and CIS) , 2014 .

[16]  T. Gerig.  Multivariate Analysis: Techniques for Educational and Psychological Research , 1975 .

[17]  Lubomir M. Hadjiiski,et al.  Bladder cancer treatment response assessment using deep learning in CT with transfer learning , 2017, Medical Imaging.

[18]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[19]  Yair Lotan,et al.  Discrepancy between clinical and pathologic stage: impact on prognosis after radical cystectomy. , 2007, European urology.

[20]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[21]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[22]  C. Metz,et al.  Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. , 1998, Statistics in medicine.

[23]  H. Chan,et al.  Auto-initialized cascaded level set (AI-CALS) segmentation of bladder lesions on multidetector row CT urography. , 2013, Academic radiology.

[24]  Antonio Finelli,et al.  Upstaging of urothelial cancer at the time of radical cystectomy: factors associated with upstaging and its effect on outcome , 2012, BJU international.

[25]  H. Herr,et al.  Quality control in transurethral resection of bladder tumours , 2008, BJU international.

[26]  E. Compérat,et al.  Guidelines on Muscle-invasive and Metastatic Bladder Cancer , 2013 .

[27]  Lubomir M. Hadjiiski,et al.  Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. , 2016, Medical physics.

[28]  N. Petrick,et al.  Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. , 1995, Physics in medicine and biology.

[29]  Derek Raghavan,et al.  Intravenous cis‐platinum for invasive bladder cancer , 1984, The Medical journal of Australia.

[30]  E. Compérat,et al.  Updated 2016 EAU Guidelines on Muscle-invasive and Metastatic Bladder Cancer. , 2017, European urology.

[31]  Yair Lotan,et al.  Characteristics and outcomes of patients with clinical T1 grade 3 urothelial carcinoma treated with radical cystectomy: results from an international cohort. , 2010, European urology.