An evaluation of the performance of the Point of Care Test iCHROMA™ AFP method: Precision and accuracy

The estimation of serum alpha-fetoprotein (AFP) is useful in the diagnosis and monitoring of primary hepatocellular carcinoma, hepatoblastoma, non-seminomatous testicular germ cell tumours and other germ cell tumours. The iCHROMA™ AFP is a lateral flow chromatography, fluorescence immunoassay (FIA) for the quantitative determination of AFP in serum or plasma. In this study, the Boditech iCHROMA™ AFP assay had a very good precision of 9.8%. It showed a very good correlation with the following 12 methods: Abbott Architect (r2 = 0.9705), BioMerieux VIDAS (r2 = 0.9717), Roche Cobas 6000/8000 (r2 = 0.9738), Siemens Centaur XP/XPT/Classic (r2 = 0.9654), Siemens/DPC/Immulite 2000/2500 (r2 = 0.9673), Siemens/DPC/Immulite 1000 (r2 = 0.9670), Beckman Dxl 600/800 (r2 = 0.9676), Roche Elecsys (r2 = 0.9683), Roche Cobas 4000/e411 (r2 = 0.9688), Roche Modular E170 (r2 = 0.9692), SNIBE Maglumi (r2 = 0.9457) and Ortho Vitros 3600/5600/ECi (r2 = 0.9714). In summary, the iCHROMA™ AFP, a rapid point of care test method, has a within-run precision value of less than 10% and excellent correlations with traditional laboratory methods. There is a consistent overestimation with the iCHROMA™ method, which must be taken into consideration when setting a reference range.

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The Potential of Artificial Intelligence to Detect Lymphovascular Invasion in Testicular Cancer

Cancers ◽

10.3390/cancers13061325 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1325

Author(s):

Abhisek Ghosh ◽

Korsuk Sirinukunwattana ◽

Nasullah Khalid Alham ◽

Lisa Browning ◽

Richard Colling ◽

...

Keyword(s):

Artificial Intelligence ◽

Testicular Cancer ◽

Germ Cell ◽

Lymphovascular Invasion ◽

Automatic Segmentation ◽

Prognostic Significance ◽

Germ Cell Tumours ◽

Testicular Germ Cell ◽

Testicular Germ Cell Tumours ◽

Whole Slide Images

Testicular cancer is the most common cancer in men aged from 15 to 34 years. Lymphovascular invasion refers to the presence of tumours within endothelial-lined lymphatic or vascular channels, and has been shown to have prognostic significance in testicular germ cell tumours. In non-seminomatous tumours, lymphovascular invasion is the most powerful prognostic factor for stage 1 disease. For the pathologist, searching multiple slides for lymphovascular invasion can be highly time-consuming. The aim of this retrospective study was to develop and assess an artificial intelligence algorithm that can identify areas suspicious for lymphovascular invasion in histological digital whole slide images. Areas of possible lymphovascular invasion were annotated in a total of 184 whole slide images of haematoxylin and eosin (H&E) stained tissue from 19 patients with testicular germ cell tumours, including a mixture of seminoma and non-seminomatous cases. Following consensus review by specialist uropathologists, we trained a deep learning classifier for automatic segmentation of areas suspicious for lymphovascular invasion. The classifier identified 34 areas within a validation set of 118 whole slide images from 10 patients, each of which was reviewed by three expert pathologists to form a majority consensus. The precision was 0.68 for areas which were considered to be appropriate to flag, and 0.56 for areas considered to be definite lymphovascular invasion. An artificial intelligence tool which highlights areas of possible lymphovascular invasion to reporting pathologists, who then make a final judgement on its presence or absence, has been demonstrated as feasible in this proof-of-concept study. Further development is required before clinical deployment.

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