Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer

Abstract

Background: Artificial intelligence-assisted image recognition technology is currently able to detect the target area of an image and fetch information to make classifications according to target features. This study aimed to use deep neural networks for computed tomography (CT) diagnosis of perigastric metastatic lymph nodes (PGMLNs) to simulate the recognition of lymph nodes by radiologists, and to acquire more accurate identification results. Methods: A total of 1371 images of suspected lymph node metastasis from enhanced abdominal CT scans were identified and labeled by radiologists and were used with 18,780 original images for faster region-based convolutional neural networks (FR-CNN) deep learning. The identification results of 6000 random CT images from 100 gastric cancer patients by the FR-CNN were compared with results obtained from radiologists in terms of their identification accuracy. Similarly, 1004 CT images with metastatic lymph nodes that had been post-operatively confirmed by pathological examination and 11,340 original images were used in the identification and learning processes described above. The same 6000 gastric cancer CT images were used for the verification, according to which the diagnosis results were analyzed. Results: In the initial group, precision-recall curves were generated based on the precision rates, the recall rates of nodule classes of the training set and the validation set; the mean average precision (mAP) value was 0.5019. To verify the results of the initial learning group, the receiver operating characteristic curves was generated, and the corresponding area under the curve (AUC) value was calculated as 0.8995. After the second phase of precise learning, all the indicators were improved, and the mAP and AUC values were 0.7801 and 0.9541, respectively. Conclusion: Through deep learning, FR-CNN achieved high judgment effectiveness and recognition accuracy for CT diagnosis of PGMLNs. Trial Registration: Chinese Clinical Trial Registry, No. ChiCTR1800016787; http://www.chictr.org.cn/showproj.aspx?proj=28515.

Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis

“…(2019) [48] * Preoperative contrast-enhanced CT images within 2 weeks before surgery; histologically confirmed primary invasive breast cancer; SLN biopsy (and ALND); pathologically results after operation confirmed SLN metastasis Neoadjuvant therapy before CT examination and surgery; poor visualization of the tumor for segmentation due to serious artifacts caused by metallic foreign bodies on the breast; tumor was too small to be seen on CT images; incomplete clinicopathological data 348/348 Training set:184/184 Testing set:164/164 Training set:71(71)/113(113) Testing set:63(63)/101(101) Training set: SLN-P:52(9;NR); SLN—N:50(11;NR) Testing set: SLN-P:50(10;NR); SLN—N:53(10;NR) NR Yuan Gao et al. (2019) [49] NR No metastatic LNs revealed by CT; with preoperative neoadjuvant radio-chemotherapy; complicated with abdominal infection; pathological grouping different from CT grouping; LN adhesions 602/38,495 NR 62(NR;20–91) 72% David Coronado-Gutierrez et al. (2019) [50] * Positive metastatic nodes by ultrasound-guided FNA or CNB; Negative metastatic nodes determined by histopathology Surgical biopsy showed positive result after not suspicious nodes in ultrasound exam or negative results of ultrasound-guided FNA or CNB; Patients refused to receive SLNB 127/118 NR(53)/NR(65) 54.6 (NR;26~91) NR Yukinori Okada et al.…”

“…(2019) [48] * LNM SLNM in Breast Cancer Breast cancer Histopathology Resampling method NO Yuan Gao et al. (2019) [49] LNM PGMLNs in GC GC Histopathology; expert consensus Resampling method NO David Coronado-Gutierrez et al. (2019) [50] * LNM Metastasis in the axillary lymph node Breast cancer Histopathology Resampling method NO Yukinori Okada et al.…”

“…(2019) [48] * CT YES NR YES CNN-F; multivariable logistic regression CNN; logistic regression YES Retrospective cohort 184/164 2016.1–2018.11 NO Yuan Gao et al. (2019) [49] CT YES NR YES FR-CNN CNN NR Cohort 1: retrospective cohort collected from Tongji Hospital Cohort 2: retrospective cohort collected from Hubei Cancer Hospital (Hubei, China) 32,495/6000 2011.1–2018.5 No David Coronado-Gutierrez et al. (2019) [50] * US YES NR YES CNN; VGG-M VGG NR Retrospective cohort NR/NR 2015.4~2018.8 NO Yukinori Okada et al.…”

Artificial intelligence in gastric cancer: Application and future perspectives

“…AI-assisted endoscopic diagnosis included the extraction of image features[ 7 , 8 ], the detection of early gastric cancer[ 9 - 14 ], the detection of precancerous conditions[ 15 ], the optimization of magnifying endoscopy with narrow-band imaging (M-NBI)[ 16 - 19 ] and the application of Raman endoscopy[ 20 , 21 ]. AI-assisted pathologic diagnosis involved the automatic identification of gastric cancer[ 22 ], the detection of gastric cancer based on the whole slide imaging (WSI)[ 23 - 26 ], the automatic detection of tumor-infiltrating lymphocytes (TILs)[ 27 ] and the segmentation of lesion regions[ 28 - 31 ], while AI-assisted CT diagnosis focused on the identification of preoperative peritoneal metastasis[ 32 ], the detection of perigastric metastatic lymph nodes[ 33 ] and two other new imaging techniques[ 34 , 35 ]. Under certain conditions, the diagnostic performance of these AI models was not inferior to human experts.…”

“…Compared to the current status of poor CT depiction in lymph node metastasis and low detection sensitivity, the novel DL-based model was expected to obtain an excellent performance of CT imaging. Gao et al[ 33 ] developed and validated faster region-based CNN based on CT images. The experimental results showed that faster region-based CNN obtained a high accuracy for the diagnosis of perigastric metastatic lymph nodes with the mean average precision value of 0.7801 and AUC value of 0.9541.…”