MATERIALS AND METHODS

Study population

Retrospectively, we selected 27 cases of thyroid nodules archived at the Pathology Laboratory of the Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP) from 2015 to 2021. Only cases with a cytological diagnosis of PTC and histological confirmation were included. Cases with suspected PTC and those with a cytological diagnosis of PTC associated with a discordant histological diagnosis were excluded. Inadequate slides, such as those heavily darkened or blurred, preventing the identification of the studied structures, were also excluded. After the selection process, 21 cases were included in the study.

Image acquisition

For the selection of cytology slides, the initial preference was given to the first slide within the archival records. In a singular case featuring bilateral PTC, two slides were chosen, each obtained from the respective thyroid lobe, resulting in a total of 22 cytology slides. These archival slides originated from ultrasound-guided FNA, wherein aspirated material was meticulously distributed onto glass slides, followed by uniform spreading using an additional glass slide. The subsequent steps included air-drying of the slides and staining via the Diff-Quik method.

Utilizing the Pannoramic 1000 (P1000) Scanner from 3D-HISTECH Ltd. (Budapest, Hungary), each selected slide was subjected to scanning, generating whole-slide images (WSIs) in MRXS file format. Subsequently, manual cropping of the WSIs was performed through the Thyroid Prediction (ThyPred) program, employing standardized augmentation at 4.92 pixels/μm and resulting in a resolution of 1,920×1,007 pixels (390.24× 204.67 μm size). These cropped images were then exported in JPEG format, amounting to a total of 7,255 images. To further optimize the model fitting process, a Python script was developed to convert the obtained images into squared dimensions with a resolution of 640×640 pixels.

Annotation of the region of interest

The image annotation process involved identifying the region of interest containing the nuclear groove within the follicular cell. This was accomplished by creating a precise bounding box using a standard mouse input method through LabelImg. The annotations were meticulously carried out by an experienced pathologist with over 20 years of expertise in the field. A nuclear groove was defined as an elongated structure that was not always linear, with no defined orientation, crossed the largest part of the cell and had a different appearance in terms of the color when compared to the rest of the nucleus. The annotations were grouped in a single class defined as “GROOVE”. From this process, 7.242 nuclear grooves were annotated and then exported as simple text files, with TXT extension, in YOLO format. A script was written to remove invalid annotation, such as repeated or no content bound boxes and no label or wrong written labels.

Data augmentation techniques

The image augmentation techniques create new image versions of a sample. In this process, the techniques of zooming, rotations and random mirroring were used, which proved to be adequate, since the model is required to identify the grooves regardless orientation or position. Therefore, the data augmentation techniques were used to artificially increase the amount of data of the database and, consequently, improve the performance of the neural network. Also, a larger database helps avoid overfitting and improve the model generalization capacity. Since deep learning techniques were used, no other methods of pre-processing the images were performed to avoid bias.

Training and validation samples

After increasing the database with the data augmentation techniques, the images were divided in two groups in a 7:3 proportion, the training group (containing 70% of the images) and the validation group (comprising the remaining 30% of the images). Images of grooves previously encountered during training were not present in the validation data. The training group was utilized for fine-tuning the pretrained model, whereas the validation group was employed to assess its performance and select the model that achieved the best results. The methodology overview is illustrated in Fig. 1.

System architecture

We opted for the use of transfer learning to develop the model, which is fine-tuned with the images collected, resulting in the groove detector model. Transfer learning is recognized as a machine learning approach wherein a pretrained model, initially trained on a dataset unrelated to the current objective, is repurposed as the foundation for a second model. This process leads to the development of a more efficient model.

The pretrained model chosen was YOLOv5s, a state-of-the-art real-time object detection, that works as a single-stage object detector, and has achieved optimal performances in its object detection datasets, the Pascal VOC (Visual Object Classes) and MS COCO. When in model training, YOLOv5 applies the mosaic data augmentation technique, which combines four training images, mixing four distinct contexts, increasing the accuracy of the network. The main idea is to use a feature extractor which encodes the images in tensors that is used to locate the object and then classified.

The YOLOv5 network consists of three main parts: the backbone, neck, and head. The backbone extracts image characteristics using convolutional and Spatial Pyramid Pooling. The neck combines features from different network levels using Path Aggregation Network to enable accurate object detection across various sizes and scales. The head comprises three convolutional layers to predict bounding boxes and object classes.

Metrics evaluation

As an evaluation of the model utilized, the loss functions and metrics of YOLOv5 were analyzed. The following loss functions were utilized: object loss and box loss. One of the loss functions of these models is the object loss, which refers to the error in detecting if an object exists in the region suggested. On the other hand, the box loss is characterized by the error between the location and size of the predicted bounding box and the true bounding box. Considering that this study evaluated only one class (“GROOVE”), the classification loss, that assesses if the classification of each predicted bounding box was correct, was not applied.

The Intersection over Union (IoU) is obtained by the division of the area of overlap between the two bounding boxes (predicted and true) by the area of their union. A true positive was considered when the IoU was superior or equal to the threshold, meaning that the model could identify an object that is present on the ground truth. False positives, on the other hand, were counted when the IoU was inferior to the threshold, indicating that the prediction has a small overlap with the true ground or has no overlap at all. False negatives occur when the true positive box is not correctly predicted. Also, considering that in object detection the background is not evaluated, true negatives were not obtained.

Precision, also called positive predicted value, is calculated by the ratio between true positives and total of positives (true positives and false positives), evaluating how correct the predicted bounding box is (i.e., the proportion of positive identifications among positive predictions).

Recall, also called sensitivity, is obtained from the division between the true positives and the sum of the true positives and the false negatives. It is a metric that evaluates the probability of the feature being positive when the result is positive, indicating how much the true bounding boxes were correctly predicted.

Average precision is calculated by the weighted mean of precisions of a class at each threshold and represents the area below the precision-recall curve. Mean average precision (mAP) refers to the mean average precision of all classes in a given threshold. When it is calculated in the IoU at the threshold of 0.5, it receives the nomenclature of “mAP_0.5”, meaning that there is 50% of overlap. The mean of the thresholds ranging from 0.5 to 0.95 is called “mAP_0.5:0.95”.

Given that the deep learning model relies on adjusting weights based on input images, we define the aggregate number of iterations performed by the training group within a training cycle, denoted as an epoch. In this work, we used 50 epochs (0 to 49), which means that the images of the training group were submitted 50 times to adjust the model weights. The model fitness is defined by the epoch that has the best value of mAP_0.5:0.95.

RESULTS

A total of 7.255 images were obtained from 22 WSI’s, in which 7.242 nuclear grooves were annotated. To increase the database provided to the model, these images were submitted to data augmentation techniques. This database was randomly divided into training and validation groups, in a ratio of 7:3. As aforementioned, in the process of training and validation of the model, 50 epochs were used (from 0 to 49).

In terms of the training group, the model demonstrated improvement in both box loss and object loss throughout the entire training period. This is evident from the observed decrease in box loss, starting at 10.2% in the initial stages of training and reaching 2.3% by the 49th epoch. Similarly, the object loss exhibited a decline from 3% at the beginning to 1.3% at the conclusion of the training.

However, the same level of improvement was not observed in the validation group, which showed an enhancement in results until peaking at the 14th epoch. During this phase, a reduction in the values of the loss functions was noted, with an object loss of 2.7% and a box loss of 6.1% at epoch 0. By the 14th epoch, the values had improved to 3.1% for box loss and 1.7% for object loss. After the 14th epoch, a gradual increase in the values of the loss functions was observed, particularly in object loss, which reached 2.7% by the 49th epoch. In contrast, box loss demonstrated a more modest increase, fluctuating between 3.2% and 3.6% until settling at a final value of 3.5% (epoch 49).

In accordance with what was observed in the validation group, the recall (sensitivity) values exhibited a decrease at the beginning of the training, ranging from 44% in epoch 0, with a valley at 37.4% in the 5th epoch and subsequent increase between epochs 13 to 21, when it presented its best results, ranging from 48.5% to 50.8%, except for the 15 and 16 epochs that obtained, respectively, 43.8% and 47%. After this period, there was a decrease in this metric, which began to exhibit, still, some degree of fluctuation between 38.5% and 47.3%. Precision (positive predictive value), on the other hand, was 8.5% at epoch 0, with a slight increase up to the interval comprised by the epochs 7 and 18, which ranged from 40.6% (epoch 10) to 45.9% (epoch 9), except for epochs 12 (39.4%) and 16 (39%). After this period, there was a reduction in the values, which ranged from 33.7% (epoch 30) to 40.6% (epoch 37), except for some epochs that presented values that exceeded this limit, namely: 27 (44.1%), 33 (44.5%), 35 (47.4%), 43 (41.6%), 45 (41.7%), and 46 (41.9%).

The mean average precision at the 0.5 threshold (mAP_0.5) increased from 7.5% at epoch 0 to 39.3% at epoch 7. Between epochs 8 and 18, the highest values of this metric were achieved, ranging from 40.4% to 45.6% (best result in training, corresponded to the epoch 14). After this period, however, a reduction in the values was observed, which ranged from 33.1% to 39.4%, except for epochs 33 (40.8%) and 35 (41.5%). A similar phenomenon was detected in the mean average precision referent to thresholds from 0.5 to 0.95 (mAP_0.5:0.95), with an increase until the 7th epoch, when it went from 3.2% to 21.3%. The epochs between 8 to 18 exhibited the best results of this metric, with the highest value, of 29.8%, found in epoch 14. After this moment, until the end of the training, a reduction in the metric was noted, which began to vary between 20.9% (epoch 30) to 25.6% (epoch 36), with some isolated high values, namely: epoch 33 (26.7%) and 35 (27.6%).

Therefore, it can be concluded that the best model was found at the 14th epoch, obtaining 67% of true positives in the detection of nuclear grooves. It also showed 45.5% for mAP_0.5, 49.8% for recall (sensitivity), 43.1% for precision (positive predictive value) and an F1 score of 0.46. The metric curves obtained during the training and validation of the model are plotted in the graphs shown in Fig. 2.

DISCUSSION

Nowadays, the test of choice for the investigation of malignancy of thyroid nodules is FNA associated with ultrasonography and subsequent pathological evaluation, since it is a minimally invasive method with high accuracy [2,6-11,34]. Considering that cytology analysis relies on the direct application of microscopic evaluation criteria by the cytopathologist, it is typically susceptible to interobserver variability [4,6,7,9,29,35]. In addition, it is a time-consuming and laborious process [6]. As a result, digital pathology has gained prominence, which opens the possibility for the application of computational algorithms to contribute to the cytopathological diagnosis [8,35,36].

The identification of nuclear grooves is an important cytological marker for several thyroid lesions [16,17,19,22,23]. Although not very specific, it has a relatively high sensibility for the diagnosis of PTC, since most cytology slides obtained from PTC have large quantities of nuclear grooves [17-19,21-23,26]. This helps to distinguish this condition from other diseases [17,22,26]. Additionally, it should be noted that not only this structure, but also other nuclear modifications have gained increased prominence in cytological diagnoses of thyroid diseases [11,12,15]. In this context, it is important to highlight that the development of similar models applied to other structures, such as nuclear pseudoinclusions, could assist in distinguishing between PTC and on-invasive follicular thyroid neoplasm with papillary-like nuclear features [37].

Computational models can contribute to standardizing cytopathological evaluations, thereby reducing both interobserver and intraobserver variability. This standardization can help minimize discrepancies in care between larger and peripheral institutes. In a study by Kuzan et al. (2021) [37], a high interobserver disagreement was demonstrated in the diagnosis of non-benign thyroid biopsies (Bethesda categories III, IV, V, and VI), revealing a percentage agreement of 50%–56.8% among evaluators, along with a general agreement of 33.6%. Additionally, the same study reported an intraobserver agreement of 62.5%–58.8% [38]. In another study conducted by Cibas et al. (2013) [38], an interobserver agreement of 64% was observed between local pathologists and central experts. The intraobserver agreement, with more than 30 days apart, ranged from 60%–83%, considering a classification system with six categories. The study also compared the accuracy of cytopathological diagnoses between the two groups mentioned above, resulting in a specificity of 40.4%–55.6%, sensitivity of 90.3%–92.0%, negative predictive value of 90.9%–89.9%, and positive predictive value of 46.8%–53.7% [38]. Moreover, when it comes to specific nuclear structures, Thomson et al. (2018) [39], while evaluating membrane irregularities (such as irregular contours, nuclear grooves, and nuclear pseudoinclusions), established a substantial agreement (k of 0.61–0.67, with standard variation of 0.15–0.26). However, a study developed by Liu et al. (2018) [40], evidenced a fair agreement for the characteristics (k of 0.228) and in the identification of nuclear grooves (k of 0.227). Thus, there exists not only a high interobserver variability in the evaluation of nuclear grooves and other nuclear structures but also variability between different institutions.

In addition, cytopathological evaluation through light microscopy is time-consuming and laborious. Despite its confirmation as an important diagnostic tool, the use of digital pathology, in general, demands a longer period to perform the diagnosis when compared to microscopy, which has constituted an important barrier in its routine use [35,42,43]. This is confirmed with the work of House et al. (2013) [41], that studied the time of diagnosis in digital and optical cytology and concluded that cytopathologists without experience in digital cytopathology spent 4.2 minutes in digital slides against 2.9 minutes in glass slides, while those who had experience in the area presented a diagnosis time of 3.1 minutes in the digital slides against 2.0 minutes in the conventional microscopy. In this sense, it is important to note that the YOLO family of object detectors, especially the YOLOv5 variants, stands out as some of the most efficient models in their class, being able to perform thousands of inferences per second, even when applied to machines with limited processing power. Therefore, it can be inferred that the application of AI-based models can contribute to a significant reduction in the diagnostic time of digital pathology [44].

Classification models that are based on the evaluation of multiple nuclear characteristics (mimicking the diagnostic experience of pathologists) obtained a high sensibility and predictive positive value for the diagnosis of malignancies. Regarding the diagnosis of PTC, Sanyal et al. (2018) [44] developed a CNN powered by a manually selected database that contained 186 microphotographs of PTC and 184 of other thyroid lesions processed in Romanowsky and Papanicolaou staining at 10× and 40× magnification. A sensitivity and positive predictive value of 90.5% and 63.3%, respectively, were obtained when the diagnosis of PTC was considered in 10× or 40× magnification; however, considering that the diagnosis needed to be made in both augments, 33.3% for sensitivity and 87.5% for positive predictive value were achieved. In the evaluation of selected images by pathologists, one criterion considered was the presence of nuclear grooves. However, there is no detailed data on the identification of specific nuclear structures; only a collective overview within the context of PTC was provided [32].

In 2019, Guan et al. [45], using 279 cytological images obtained from liquid preparations processed with hematoxylin and eosin photographed at 40× magnification, applied two different architectures (VGG-16 and Inception-v3) evaluating a variety of nuclear characteristics (contours, perimeter and area of average pixel intensity), obtaining a sensitivity of 98.5%–100% and a positive predictive value of 89.5%–95.8% also in the diagnosis of PTC.

Recently, Duan et al. (2022) [46] developed a two-stage CNN, with an object detection module (based on YOLOv4, with malignant thyroid regions manually annotated by pathologists) and a classification module (based on EfficientNet), which used 360 WSI with liquid-based cytological preparation, that were divided into “positive” (including 222 images classified as Bethesda 1) and “negative” (including 10 images with Bethesda 2, 2 images with Bethesda 3, 36 images with Bethesda 36 and 90 images with Bethesda 6), totaling 29.780 images for the object detection module and 63.356 images for the classification module; with this model, 97.9% for F1 score, 98.08% for recall and 97.83% for precision were obtained for the proposed classification [46].

Also, using 367 Papanicolaou stained images photographed at 40× magnification, with an ensemble deep learning methods and stain normalization, Nguyen et al. (2022) [47] improved the automatic classification of PTC cell clusters from FNA, obtaining a mean sensitivity of 0.97 using DenseNet161 [47].

To the best of our knowledge, this is the only work whose objective was to evaluate exclusively the presence of the nuclear groove in images obtained from a Diff-Quik stained digitized slide, using an object detection model (CNN based on YOLOv5 architecture).

In this investigation, we illustrated the potential for detecting nuclear alterations, such as grooves in digitalized slides stained with Diff-Quik, utilizing computer vision and deep learning. While our findings suggest partial feasibility of this approach, it is imperative to acknowledge significant limitations that may impact the robustness of the results obtained. However, these limitations do not preclude the potential for identifying nuclear structures through an AI-based approach.

This assertion is substantiated by the model’s development of a rule governing true bounding boxes, signifying its capacity to extract defining characteristics of a nuclear groove, resulting in a commendable true positive rate of 67%. Despite the identified limitations, the model demonstrated a partial ability to locate and scale the object under study, evidenced by a mAP of 45.5%, sensitivity of 49.8%, and positive predictive value of 43.1%.

While these results indicate promise, it is essential to interpret them with caution considering the acknowledged constraints. The study suggests that, despite limitations, utilizing an AI approach for identifying nuclear structures remains a viable avenue, paving the way for further refinement and exploration in future research endeavors.

Also, in our results, it is crucial to emphasize the observed improvement in metrics within the training group over the 50 epochs. This trend, characterized by a gradual decrease in the values of the loss functions, is not uncommon and aligns with expectations for models of this nature. This phenomenon arises because, with each epoch, the model undergoes a new cycle using the same images from the training group. The iterative adjustment of weights based on these training images naturally leads to a reduction in errors.

However, it is noteworthy that the concurrent increase in the loss functions associated with the validation group, particularly after the 14th epoch, signals the onset of overfitting. This phenomenon indicates that the model has become excessively attuned to the specific data within the training group, compromising its ability to generalize to untrained data in the validation group. Another indicative factor for the onset of overfitting is the presence of fluctuating recall and accuracy values.

Although the aforementioned studies varied in terms of the model’s application, employing a classification system, our results hint at the limitations inherent in our own study. The relatively small database utilized may have influenced the obtained results, potentially diminishing the model’s generalization capacity. Moreover, the fact that annotation was conducted by a single pathologist could be considered a limitation. The annotation task demands extensive review and meticulous attention to detail. Additionally, the utilization of only MGG-stained cytology may have affected the outcomes. While nuclear grooves are typically visualized in most PTC cytology MGG-stained slides, this staining method primarily highlights cytoplasmic detail, potentially compromising the visualization of nuclear structures [24-26]. Furthermore, considering that the evaluation of nuclear structures constitutes an important cause of diagnostic disagreement between optical and digital observation of cytology, the annotations may not have been the most representative [48].

It is possible to observe that the potential of the application of AI is enormous. This phenomenon becomes specially promising in thyroidology. However, there are several challenges for the implementation, in practice, of computational-based models [35]. In this regard, some obstacles that are still encountered include the development of the model, the quality and representativeness of the data, the interpretability, the validation of the algorithm and the clinical adoption [28].

The application of CNNs and deep learning is feasible. In this regard, considering that the methodology used can be applied in the identification of the most diverse nuclear and cellular characteristics, new perspectives might be used to increase the accuracy of the exam. Also, the association with other nuclear structures seems to improve the possible applicability. In addition, the results show the importance of the quality of the database used for the fine tuning of the model. Therefore, notable improvements might occur with the resolution of the points mentioned above.

Fig. 1.

Methodology summary. (A) Digitalized image number 20 in smaller augment (size of the slide: 24.93 mm × 44.13 mm) on the ThyPred program. (B) Digitalized image number 20 in larger augment (size of the visible slide: 390.24 μm × 204.47 μm) on the ThyPred program, with a nuclear groove identified on its center. (C) Squared image with resolution of 640 × 640 pixels, obtained after the script execution. (D) Manual annotation of the nuclear groove on LabelImg program. (E) Images used on the training of the model after data augmentation techniques. (F) Annotated nuclear grooves (true bounding box) in order to compare with the predicted on the validation test (predicted bounding box). (G) Predicted nuclear grooves by the execution of the model (predicted bounding box).

Fig. 2.

Loss functions, precision, recall and mean average precision (mAP) graphs obtained from the training and validation groups per epoch. (A) Train box loss: error between the location and size of the predicted bounding box and the true bounding box in the training group. (B) Train object loss: error in detecting if an object exists in the region suggested in the training group. (C) Recall: recall obtained from the model. (D) Precision: precision obtained from the model. (E) Validation box loss: error between the location and size of the predicted bounding box and the true bounding box in the validation group. (F) Validation object loss: error in detecting if an object exists in the region suggested in the validation group. (G) mAP_0.5: mean average precision at threshold 0.5. (H) mAP_0.5:0.95: mean average precision from the threshold 0.5 to 0.95.

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