Deep learning & CNN

Algorithm differentiates small renal masses on multiphase CT

A deep learning method with a convolutional neural network (CNN) can support the evaluation of small solid renal masses in dynamic CT images with acceptable diagnostic performance.

Photo
67-year-old woman with 2.7-cm solid renal mass that was pathologically diagnosed as clear cell renal cell carcinoma after surgery. Unenhanced (A), corticomedullary phase (CMP) (B), nephrogenic phase (NP) (C), and excretory phase (EP) (D) CT images all show small solid renal mass correctly diagnosed as malignant lesion by CMP, triphasic (CMP, NP, and EP), and all-phase convolutional neural network models. Readers 1 and 2 also correctly determined small solid renal mass to be malignant (both 4 on 5-point scale).

The article was published in the March issue of the American Journal of Roentgenology (AJR).

Between 2012 and 2016, researchers at Japan’s Okayama University studied 1807 image sets from 168 pathologically diagnosed small (≤ 4 cm) solid renal masses with four CT phases—unenhanced, corticomedullary, nephrogenic, and excretory—in 159 patients. 

Masses were classified as malignant (n = 136) or benign (n = 32) using a 5-point scale, and this dataset was then randomly divided into five subsets. 

As lead AJR author Takashi Tanaka explained, “four were used for augmentation and supervised training (48,832 images), and one was used for testing (281 images).”

Photo
41-year-old woman with 2.6-cm solid renal mass that was pathologically diagnosed as angiomyolipoma after biopsy. A, Unenhanced (A), corticomedullary phase (CMP) (B), nephrogenic phase (C), and excretory phase (D) CT images show small solid renal mass correctly diagnosed as benign lesion by CMP convolutional neural network (CNN) model but not by other CNN models. Readers 1 and 2 incorrectly determined small solid renal mass to be malignant (4 and 5 on 5-point scale, respectively).

Utilizing the Inception-v3 architecture CNN model, the AUC for malignancy and accuracy at optimal cutoff values of output data were evaluated in six different CNN models. Finding no significant size difference between malignant and benign lesions, Tanaka’s team did find that the AUC value of the corticomedullary phase was higher than that of other phases (corticomedullary vs excretory, p = 0.022).

Additionally, the highest accuracy (88%) was achieved in the corticomedullary phase images. Multivariate analysis revealed that the CNN model of corticomedullary phase was a significant predictor for malignancy, “compared with other CNN models, age, sex, and lesion size,” Tanaka concluded.


Source: American Roentgen Ray Society (ARRS)

13.01.2020

Read all latest stories

Related articles

Photo

Algorithm-assisted diagnostics

AI in imaging: not as reliable as you'd think

Machine learning and AI are highly unstable in medical image reconstruction, and may lead to false positives and false negatives, a new study suggests. A team of researchers, led by the University of…

Photo

AI in radiology

Deep learning helps visualize X-ray data in 3D

A team of scientists at Argonne National Laboratory has leveraged artificial intelligence to train computers to keep up with the massive amounts of X-ray data taken at the Advanced Photon Source.

Photo

Deep Learning in Radiology

New Levels of Precision with Self-learning Imaging Software

The complex form of machine learning DLIR (Deep Learning Image Reconstruction) is based on a deep neuronal network which is similar to the human brain. The artificial neurons of this network learn…

Related products

Canon - HIT Automation Platform

Artificial Intelligence

Canon - HIT Automation Platform

Canon Medical Systems Europe B.V.
Fujifilm · REiLI

Artificial Intelligence

Fujifilm · REiLI

FUJIFILM EUROPE GmbH
MinFound – ScintCare CT128

Volume CT

MinFound – ScintCare CT128

MinFound Medical Systems Co., Ltd
RTI Group – Piranha

Testing Devices

RTI Group – Piranha

RTI Electronics
Siemens Healthineers – Somatom Edge Plus

Volume CT

Siemens Healthineers – Somatom Edge Plus

Siemens Healthineers
AB-CT – Advanced Breast-CT – nu:view

Mammo CT

AB-CT – Advanced Breast-CT – nu:view

AB-CT – Advanced Breast-CT GmbH
Subscribe to Newsletter