Cloud Hosting

In this study, a combination of RI image data and color Papanicolaou-stained image data improved the accuracy of MLA for diagnosing cancer using thyroid FNAB specimens. The classification results of the MLA using color Papanicolaou-stained images were highly dependent on the size of the nucleus, but those of the MLA using RI images were less dependent on nucleus size and were affected by information around the nuclear membrane. The final algorithm using data from both types of images together distinguished thyroid cell clusters from benign thyroid nodules and PTC with 100% accuracy.

MLA has shown superior diagnostic performance using images of thyroid FNAB specimens when a convolutional neural network (CNN) architecture was adopted, which is effective for image analysis7,8,12,13. Guan et al.13 studied a CNN-based MLA for classifying hematoxylineosin-stained FNAB specimens of benign thyroid nodule and PTC (TBSRTC II, V and VI). A total of 887 fragmented color images were used in this study, which were cropped from 279 images taken using a digital camera attached to a brightfield microscope. The trained algorithm exhibited 97.7% accuracy for distinguishing between 128 test images of benign and malignant nodules. Range et al.8 used MLA to classify Papanicolaou-stained FNAB specimens of broader spectrum thyroid nodules (TBSRTC IIVI). They used 916 color images obtained using a whole slide scanner. The trained MLA distinguished malignant from benign nodules with high accuracy (90.8%), comparable to that of a pathologist. Similarly, a CNN-based MLA performed well in our study, exhibiting high-accuracy patch-level classification (97.3%) and cluster-level classification (99.0%), using only color Papanicolaou-stained images.

However, given that the purpose of FNAB is to determine whether to operate on thyroid nodules, it must not only exhibit high overall accuracy, but also minimize serious misclassification, such as classification of an obvious malignancy as benign or that of an overtly benign nodule as a malignancy. In Guans study, MLA misclassified some cases that a pathologist classified as obviously benign as a malignancy. Similarly, in Ranges study, MLA misclassified some clearly benign nodules as malignant or misclassified a malignant nodule that was indicated for surgery as benign8. These issues are problematic because they can lead to an erroneous treatment plan for patients who would receive proper treatment if they underwent the current standard care. We studied nodules with relatively distinct benign or malignant characteristics (TBSRTC II, V, and VI). Our findings that RI data improved the accuracy of MLA in these nodules have important clinical significance since these indicate a potential reduction in the aforementioned serious misclassification.

Guan et al.13 suggested that the significant misclassifications of MLA for the thyroid FNAB specimens could be related to the nucleus size. In their study, the cells in false-positive cases showed large nuclei with a high mean pixel color information similar to malignant cells, but the pathologist determined that these cells had a typically benign morphology. The authors interpreted that the classification of MLA was based on the size and staining of the nucleus, but not on the shape. Furthermore, in our results, MLA based on color images showed limitations in accurately classifying benign thyroid cells with a large nucleus or malignant thyroid cells with a small nucleus because the size of the nucleus was the main feature required for classification. However, MLA classification based on the RI image was less affected by nucleus size. This suggests that RI images for can compensate for the limitations of MLA using color images for FNAB specimens whose nuclear size is not typical for benign or malignant cells.

Further results from analyses to explain the models suggest that RI-image based MLA uses the structure and shape of the nucleus for classification. In addition to the algorithm being activated mainly for large nuclei in color images, the algorithm was activated not only by large nuclei but also by nuclei with a clear structure in RI images. The certainty of the MLA classification results was proportional to the detail of the information around the nuclear membrane when based on RI images, but not when based on color images. Detailed nuclear structures, such as nuclear membrane irregularity and micronucleoli are important indicators of thyroid cancer diagnosis26. Thus, the accuracy of MLA classification can be improved when such information is incorporated.

Another potential strength of RI images is the integration of information of a wide vertical space. In a thyroid cytology specimen, cells are scattered over a wide vertical space (i.e. multiple z-plains) rather than over a plane. A single layer (z-plain) 2D image cannot address this vertical spread, and information from out-of-focus cells is likely to be lost or distorted. In contrast, in the RI image obtained through ODT, cells located in different Z-plains are in focus simultaneously. In our study, MLA based on color images showed a false positive result for some out-of-focus patches, whereas MLA based on RI image showed a true negative result for the same image patches (data not shown). However, the out-of-focus area is only a part of the color images, and the use of multiple z-plane images did not improve the accuracy of MLA when compared to the use of a single z-plane image in a previous study8. Therefore, it is unclear whether the aforementioned factor significantly affects the accuracy of MLA.

This study has certain limitations. Despite the large number of sample measurements, this study was performed in a single center and could not cover all conditions of specimens that could exist in real clinical environments. ODT provides optimal RI imaging in un-manipulated living cells27, but we obtained RI images from chromatically stained cells. Staining acted as an extrinsic noise or artifact in the RI images, which reduced the accuracy of MLA. Further study is required to determine the effect of staining on the outcomes. Finally, up to 30% of FNABs may have indeterminate cytopathology (TBSRTC III and IV). This study targeted specimen characteristic of benign or malignant thyroid nodules (TBSRTC II, V, and VI), and therefore, the currently trained algorithm cannot be directly applied to TBSRTC III and IV specimens without relevant training.

To investigate the complementary nature of RI images and color images, a 2D MIP image was generated by projecting the 3D RI image along the z-axis, thereby excluding the influence of dimensionality. Previous studies in the field of cell classification have demonstrated improved performance when using 3D RI images compared to 2D images28,29. Although our research did not incorporate 3D images due to the specific research objectives, we plan to expand our investigations in future studies by incorporating 3D RI images and other 3D imaging modalities.

In this study, we demonstrated the efficacy of multiplexing of RI with standard brightfield imaging using a single ODT platform for MLA-based classification of benign and malignant thyroid FNABs. Multiplexed ODT showed promise for the development of a more accurate classification of thyroid FNABs while reducing the inherent uncertainty and error observed in the current diagnostic standards. Thus, an ODT-based MLA may potentially contribute to an improved cost-effective and rapid point-of-care management of thyroid malignancies.

See the rest here:
Machine-learning-based diagnosis of thyroid fine-needle aspiration ... - Nature.com