Evaluation of one-step nucleic acid (OSNA) molecular assay for intra-operative diagnosis of sentinel lymph node metastasis.

2012 ◽  
Vol 30 (27_suppl) ◽  
pp. 194-194
Author(s):  
Shramana Mitul Banerjee ◽  
Timothy Davidson ◽  
Nikolaus Michalopoulos ◽  
Nuala McDermott ◽  
Soha El Sheikh ◽  
...  

194 Background: One step nucleic acid (OSNA) is a recently developed molecular diagnostic assay to detect lymph node metastases. Aim of the present study was to assess OSNA for intra-operative detection of sentinel node metastases with use of standard histopathology as the “gold standard.” Moreover, discordant cases were further investigated to assess their impact on patients’ management. Methods: 157 breast cancer patients with clinically and ultrasonographically negative axilla underwent axillary staging with sentinel node (SLN) biopsy. 244 SLNs were evaluated by OSNA and standard histopathology (alternate slices were submitted to OSNA and standard test). The “turn-around time” taken for an intra-operative OSNA result was recorded. Results: Sensitivity and specificity of OSNA were 92.3% and 95.6% respectively with positive predictive value of 80% and negative predictive value of 98.4%. The average time taken for an intra-operative result in this group was 34 minutes. Concordance rate was 95% while 12/244 nodes showed discordant results between OSNA and Histology. The discordant cases were re-evaluated by histopathologists and characteristics of this subgroup analysed. Patient’s management was altered by OSNA alone in 5 patients as they underwent axillary lymph node dissection on the basis of a positive OSNA when histopathology did not show evidence of metastasis. Two patients with nodes negative on OSNA had histopathology results showing metastases requiring delayed axillary clearance. The rest of the discordant nodes belonged to patients who had other nodes biopsied concurrently that were concordant in OSNA and histology. Conclusions: OSNA is a fast, valuable and highly accurate standardised method for the intra-operative evaluation of axillary lymph node metastasis and is part of our routine intra-operative assessment tool for sentinel nodes in breast cancer.

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 757
Author(s):  
Sanaz Samiei ◽  
Renée W. Y. Granzier ◽  
Abdalla Ibrahim ◽  
Sergey Primakov ◽  
Marc B. I. Lobbes ◽  
...  

Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51–68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41–0.74 and 0.48–0.89 in the training cohorts, respectively, and between 0.30–0.98 and 0.37–0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.


2004 ◽  
Vol 87 (2) ◽  
pp. 75-79 ◽  
Author(s):  
Osamu Watanabe ◽  
Tadao Shimizu ◽  
Hiroshi Imamura ◽  
Jun Kinoshita ◽  
Yoshihito Utada ◽  
...  

2016 ◽  
Vol 7 (1) ◽  
pp. 37-41 ◽  
Author(s):  
San-Gang Wu ◽  
Zhen-Yu He ◽  
Hong-Yue Ren ◽  
Li-Chao Yang ◽  
Jia-Yuan Sun ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lianhua Zhang ◽  
Zhiying Jia ◽  
Xiaoling Leng ◽  
Fucheng Ma

This paper aimed to investigate the application of ultrasound image segmentation technology based on the back propagation neural network (BPNN) artificial intelligence algorithm in the diagnosis of breast cancer axillary lymph node metastasis, thereby providing a theoretical basis for clinical diagnosis. In this study, 90 breast cancer patients with axillary lymph node metastasis were selected as the research objects and rolled randomly into an experimental group and a control group. Besides, all of them were examined by ultrasound. The BPNN algorithm for the ultrasound image segmentation diagnosis method was applied to the patiens from the experimental group, while the control group was given routine ultrasound diagnosis. Thus, the value of this algorithm in ultrasonic diagnosis was compared and explored. The results showed that when the number of hidden layer nodes based on the BPNN artificial intelligence algorithm was 2, 3, 4, 5, 6, 7, and 8, the corresponding segmentation accuracy was 97.3%, 96.5%, 94.8%, 94.8%, and 94.1% in turn. Among them, the segmentation accuracy was the highest when the number of hidden layer nodes was 2. The correlation of independent variable bubble plot analysis showed that the presence or absence of capsules, the presence of crab feet or burrs in breast cancer lesions was critical influencing factors for the occurrence of axillary lymph node metastasis, and the standardized importance was 99.7% and 70.8%, respectively. Besides, the area under the two-dimensional receiver operating characteristic (ROC) curve of the BPNN artificial intelligence algorithm model classification was always greater than the area under the curve of manual segmentation, and the segmentation accuracy was 90.31%, 94.88%, 95.48%, 95.44%, and 97.65% in sequence. In addition, the segmentation specificity of different running times was higher than that of manual segmentation. In conclusion, the BPNN artificial intelligence algorithm had high accuracy, sensitivity, and specificity for ultrasound image segmentation, with a better segmentation effect. Therefore, it had a better diagnostic effect for breast cancer axillary lymph node metastasis.


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