Analysis of features of papillary thyroid carcinoma on color Doppler ultrasound images: implications for lymph node metastasis

Abstract

Background

This study aimed to describe the color Doppler flow features of papillary thyroid carcinoma (PTC) and to further investigate the associations between these features and lymph node metastasis (LNM).

Methods

A retrospective analysis of the clinical data of 287 PTC patients confirmed by postoperative pathology at the Second Affiliated Hospital of Xi’an Jiaotong University from January 2022 to April 2023 was conducted. The Adler grading system and novel blood flow patterns were used to analyze the vascularity of the PTC lesions on color Doppler images. Univariate and multivariate logistic regression analyses were conducted to evaluate the independent effects of blood flow characteristics on LNM, and a logistic regression model was established to assess their predictive value for PTC-related LNM.

Results

In all, 287 PTC lesions were analyzed using color Doppler ultrasonography, which identified five main reference patterns: avascular (26.13%), dot-line (24.74%), branching (14.29%), garland (11.50%), and rich-disorganized (23.34%). The Adler blood flow grading was as follows: 0 (32.75%), I (18.82%), II (19.16%), and III (29.27%). A univariate analysis revealed that the Adler grade was not significantly associated with LNM (P > 0.05), whereas the garland pattern was significantly associated with LNM (P < 0.05). A multivariate analysis revealed that the garland pattern was an independent protective factor for LNM (OR [95% CI] = 0.386 [0.156–0.893]). The incorporation of the garland pattern into the model improved the predictive accuracy for LNM in PTC patients, and the AUC increased from 0.727 [95% CI: 0.669–0.786] to 0.767 [95% CI: 0.731–0.821].

Conclusions

This study classifies PTC into five types on the basis of color Doppler flow features and highlights the garland pattern as a potential predictor of LNM risk.

Background

Thyroid cancer is the most common endocrine malignancy, and papillary thyroid carcinoma (PTC) accounts for approximately 80% of all thyroid cancers [1, 2]. Although PTC lesions are generally indolent, lymph node metastasis (LNM) can occur in the early stage of the disease and is correlated with local recurrence and a poor prognosis [3]. The neovascularization process is critical to tumor growth and metastasis [4]. Color Doppler ultrasound (CDUS), an economical, simple, and noninvasive imaging tool, has been widely used for the assessment of tumor vascularity and blood flow characteristics. Therefore, a deeper analysis of the CDUS imaging features of PTC and their relationship with LNM can not only aid in the early diagnosis of PTC but can also provide a basis for assessing the risk of LNM, thus facilitating personalized disease management decisions.

Although numerous studies have used CDUS to assess blood flow in PTC, the characteristics of blood flow remain an ongoing area of research and debate [5,6,7], and a widely accepted and standardized classification system has yet to be established. Commonly used blood flow grading systems, such as the Lagalla [8] and Adler [9] classifications, are designed primarily for the evaluation of thyroid nodules and do not fully account for the unique blood flow features of PTCs. As a result, these grading systems may have certain limitations when applied to the blood flow assessment of PTC, particularly regarding the accurate identification of high-risk lesions with LNM, which could affect the precision of clinical decision-making. Although some studies have revealed the specific manifestations of blood flow in PTC [10, 11], these studies have failed to cover its diversity. To date, we still lack a classification method that can comprehensively reflect the blood flow features of PTC, which limits the clinical application of blood flow features in predicting the risk of LNM.

Therefore, this study aims to systematically summarize the sonographic features of PTC under CDUS and to explore the relationship between blood flow patterns and LNM. By introducing a new blood flow classification system, we hope to provide a novel imaging reference for assessing the invasiveness of PTC and predicting the risk of LNM.

Materials and methods

Study subjects

We reviewed the medical records of thyroid cancer patients who were admitted to the Second Affiliated Hospital of Xi’an Jiaotong University between January 2022 and April 2023. In all, 287 patients with 287 lesions were included in the study. The largest lesion in each patient was selected for analysis. Among the 287 patients, 70 were male (24.4%), with ages that ranged from 18 to 65 years (mean age 43.7 ± 10.6 years), while 217 were female (75.6%), with ages that ranged from 10 to 77 years (mean age 45.2 ± 12.3 years). The inclusion criteria were as follows: successful total or partial thyroidectomy; functional neck lymphadenectomy or radical lymphadenectomy; a diagnosis of PTC confirmed by postoperative pathology; and complete preoperative ultrasound images and clinical data. The exclusion criteria were as follows: the presence of other malignant tumors; a history of fine needle aspiration (FNA) before ultrasound examination in our hospital; a history of radiotherapy or chemotherapy; and lymphadenopathy due to different factors. This study was approved by the Hospital Ethics Committee (no. 2023503). Informed consent was obtained from all patients, and all ethical issues were strictly respected.

Equipment and data collection

Ultrasound examinations were performed with the Hitachi Ascendus ultrasound diagnostic system with an EUP-L75 probe operating at a frequency range of 5–18 MHz. Preoperative ultrasound scans were conducted by a senior physician (with 15 years of experience) in the Department of Ultrasound. The patient was placed in a supine position with a pillow placed under the neck to achieve slight hyperextension. Both transverse and longitudinal scans of the bilateral thyroid lobes and isthmus were performed to acquire multiple grayscale ultrasound and CDUS images of the thyroid lesions. The ultrasound system was preset for thyroid examination, and adjustments were made to the depth, gain, and focus, as necessary, to optimize image quality. The grayscale ultrasound features, including size, composition, echogenicity, shape, margins, and echogenic foci, were recorded according to the ACR TI-RADS lexicon. CDUS images were obtained by fine-flow imaging technology. This advanced imaging technique, which differs from conventional color Doppler, allows for the detection of low-velocity blood flow and small vessels. This technique also improves image quality by reducing common artifacts through optimized signal processing algorithms. The physical settings for color Doppler imaging were a low-pass filter, a Doppler gain set to 80–90, and a velocity scale adjusted to approximately 1.5–2.5 cm/s. During the procedure, the operator avoided the application of pressure on the lesions to prevent interference with the blood flow signals. All color Doppler images were exported in JPEG format. Patient data, including age and sex, were collected from the medical records. The number of lesions, HT status, and LNM status were confirmed through pathological findings.

Analysis of CDUS imaging characteristics

We systematically analyzed CDUS images of 287 lesions. Blood flow was classified according to the Adler grading system, as follows: Grade 0 (avascular): no detectable flow; Grade I (minimal): 1–2 flow pixels; Grade II (moderate): one main vessel or several small vessels; Grade III (marked): ≥4 distinct vessels [9]. Furthermore, two senior sonographers with > 10 years of thyroid imaging experience independently categorized the images into five predominant vascular patterns through meticulous observation and clinical experience, with a focus on vascular morphology, spatial distribution, and flow abundance. The key features of each pattern were thoroughly discussed until a consensus was reached. The five patterns included the following: avascular: no visible blood flow signal; dot-line: small local blood flow signals appearing as dots or short rods; branching: vessels appear in a branching pattern, extending toward the center of the lesion; garland: blood flow signals radiate concentrically at the lesion’s edge, with little to no blood flow in the center; and rich-disorganized type: dense, irregular, and chaotic blood flow signals within the lesion. To minimize observer bias, we implemented a rigorous dual-blind validation protocol. Both readers conducted retrospective, independent analyses, with initial concordance achieved in 84.3% of the cases (κ = 0.80, p < 0.001). Discordant interpretations (15.7%) were resolved through joint re-evaluation with reference to established diagnostic criteria. The subjective classification flowchart of blood flow patterns is shown in Fig. 1.

Fig. 1

Flowchart of the classification process of blood flow patterns in PTC

Statistical methods

In this study, statistical analyses were performed using SPSS version 27.0 (IBM, Armonk, NY, USA) and R software (version 4.3.2). Continuous variables are expressed as the mean ± standard deviation (X̄± S), and categorical variables are presented as frequencies (n, %). For continuous variables with a normal distribution, comparisons between two groups were made using independent samples t-tests, and comparisons among multiple groups were made using one-way analysis of variance (ANOVA). For nonnormally distributed continuous variables, the Mann‒Whitney U test was used for two-group comparisons, and the Kruskal‒Wallis H test was used for comparisons among multiple groups. Multivariate logistic regression was performed to analyze the risk factors for LNM. In the multivariate regression analysis, LNM was set as the dependent variable, and variables with statistical significance in the univariate and multivariate analysis were included as independent variables. To assess the predictive value of the garland blood flow pattern, we constructed logistic regression models that included and excluded the garland pattern. Mode l evaluation included decision curve analysis (DCA) and calibration curves as well as the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). Additionally, nomograms were used to visualize the best model, and DeLong’s test was applied to compare the differences in the ROC curves of different models. Unless otherwise specified, a significance level of α = 0.05 was used for all tests.

Results

General information and ultrasonic characteristics

The clinical data and ultrasound characteristics of the patients are detailed in Table 1. The detection rate of blood flow signals by CDUS was 73.9% (212/287). The 287 lesions were classified into five categories according to the new blood flow classification system proposed in this study: avascular (26.13%), dot-line (24.74%), branching (14.29%), garland (11.50%), and rich-disorganized (23.34%) (Fig. 2). According to the Adler grading system, the lesions were classified into four levels: Grade 0 (32.75%), Grade I (18.82%), Grade II (19.16%), and Grade III (29.27%) (Fig. 3).

Table 1 Descriptive analysis and analysis of differences in the baseline characteristics of 287 patients with PTC

Fig. 2

Color Doppler (top) and grayscale (bottom) ultrasound images of the five blood flow patterns. A avascular pattern showing no significant blood flow around or within the lesion. B dot-line pattern showing spot and short rod vessels around or inside the lesion. C branching pattern showing long blood vessels (indicated by white arrows), nearly or exceeding the lesion’s radius, and extending the lesion in a radial arrangement. D garland pattern showing red and blue blood flow signals concentrated around the focal area, with no central blood flow. E rich-disorganized pattern showing dense red and blue blood flow signals visible, with disordered intratumoral vessels connected

Fig. 3

Illustration of the four-grade blood flow signal classification according to the Adler grading system. A: absent vascularity (grade 0). B: minimal vascularity (grade I). C: moderate vascularity (grade II). D: marked vascularity (grade III)

Analysis of variations in PTC lesion size by blood flow type

Lesion size is regarded as an important factor that predicts the invasiveness of PTC and the risk of LNM [12]. Thus, we analyzed the differences in lesion size across different blood flow types. Table 2 shows marked differences in the sizes of the lesions with different blood flow types (P < 0.001). The pairwise comparison demonstrated that the lesions with dot-line, branching, and rich-disorganized blood flow were significantly larger than those without blood flow (P < 0.05). The lesions with rich-disorganized blood flow were significantly larger than those with dot-line and branching patterns (P < 0.05).

Table 2 Analysis of lesion size variations in 287 PTCs by blood flow type

Construction and results of the multivariate logistic regression model

The 287 PTC patients were divided into the cervical LNM group (n = 145, 50.52%) and the noncervical LNM group (n = 142, 49.48%). The univariate analysis revealed significant differences in patient age, tumor diameter, number of lesions, and sex according to LNM status (P < 0.05). No statistically significant differences were found for the other factors (P > 0.05) (Table 1). In the multivariate logistic regression analysis with LNM as the dependent variable, age, tumor diameter, number of lesions, and the garland pattern were significantly associated with LNM (P < 0.05), whereas sex was not significantly associated (P > 0.05) (Table 3). Variables with statistical significance in both the univariate and multivariate analyses were included in the final model. In Model 1, age (OR [95% CI] = 0.938 [0.915–0.961]) and the garland pattern (OR [95% CI] = 0.386 [0.156–0.893]) were protective factors for LNM, whereas tumor diameter (OR [95% CI] = 1.116 [1.063–1.179]) and lesion number (OR [95% CI] = 2.069 [1.210–3.672]) were risk factors for LNM. Model 2 was similar, as age (OR [95% CI] = 0.941 [0.917–0.964]) still served as a protective factor for LNM, but tumor diameter (OR [95% CI] = 1.118 [1.066–1.181]) and lesion number (OR [95% CI] = 2.110 [1.230–3.662]) were risk factors for LNM (Table 4). The ROC curves of Model 1 (AUC, 95% CI = 0.767, 0.731–0.821) and Model 2 (AUC, 95% CI = 0.727, 0.669–0.786) were compared using the DeLong test. The results indicated that the inclusion of the garland pattern in Model 1 significantly improved its discriminatory ability compared with when the garland pattern was excluded (Fig. 4). With respect to the other model performance metrics, the best model, Model 1, had an accuracy of 0.704 (95% CI, 0.702–0.705), a sensitivity of 0.693 (95% CI, 0.513–0.773), a specificity of 0.817 (95% CI, 0.753–0.881), a positive predictive value of 0.768 (95% CI, 0.690–0.846), a negative predictive value of 0.663 (95% CI, 0.593–0.733), and a cutoff value of 0.573. The results of the other models and the univariate analysis are also presented in Fig. 4; Table 5.

Table 3 Multivariate analyses of factors associated with LNM in PTC

Table 4 Multivariate logistic regression results for LNM in PTC patients

Fig. 4

ROC curve of the logistic regression model for the prediction of LNM. Model 2 includes the variables age, tumor diameter (mm), and number of lesions, while Model 1 includes the Garland pattern on the basis of Model 2. 95% CI For Model 1 and Model 2 (95% CI difference): 0.0005–0.0792, Z = 1.988, P = 0.047

Table 5 Model performance metrics for the prediction of LNM in PTC

Prediction nomogram for LNM in PTC and its validation

On the basis of the aforementioned results, we plotted the clinical decision curve, calibration curve, and nomogram for the optimal model. In the DCA, the vertical axis represents the net benefit, whereas the horizontal axis represents the risk threshold. The results show that when the risk threshold is approximately 0.2, the red curve corresponding to the nomogram model (Model 1) consistently lies above both the “all” model and the “none” model. This finding indicates that the model incorporating the garland pattern provides a greater net benefit at this risk threshold, which suggests its potential clinical application value (Fig. 5). The calibration curve, in which the predicted values closely overlapped with the 45° line, demonstrated excellent calibration, which indicates that the model’s predictions closely aligned with the actual outcomes (Fig. 6). The nomogram visually represents the regression coefficients from Model 1 shown in Table 4 (Fig. 7).

Fig. 5

Clinical decision curve analysis. The x-axis shows the high-risk threshold, and the y-axis shows the net benefit

Fig. 6

Bootstrap method. The x-axis shows the probability of LNM status predicted by the nomogram, and the y-axis shows the actual ratio of the LNM status. The reference line is dashed, indicating apparent calibration

Fig. 7

Nomogram for predicting the risk of LNM status. The linear predictor is the coordinate axis of the linear prediction value, which is transformed into the corresponding probability value through a conversion function

Discussion

This study presents a novel blood flow classification system to describe the blood flow characteristics of PTC and explores the role of these features in predicting LNM in PTC patients. Our findings indicate that among the five blood flow patterns, the garland pattern is an independent protective factor for LNM (OR [95% CI] = 0.386 [0.156–0.893]). Furthermore, we observed that the inclusion of the garland pattern significantly improved the predictive accuracy for LNM in conventional PTC models, as the AUC increased from 0.727 [95% CI: 0.669–0.786] to 0.767 [95% CI: 0.731–0.821], a change that was statistically significant (p < 0.05, DeLong’s test).

Ultrasound is the preferred imaging method for the diagnosis of thyroid cancer [13]. Several national and international professional organizations have developed risk stratification systems for use in ultrasound [14]. However, the present RSSs for thyroid nodules are based on grayscale ultrasound, and CDUS has not been widely adopted as a complementary technique [15]. This is primarily because numerous studies of CDUS for thyroid nodules have reported mixed conclusions. The mixed conclusions reported in these studies were primarily the result of differences in Doppler parameters and the classification of blood flow. “Abundant blood flow within the nodule” as an indicator of malignancy was removed from the 2015 ATA [16] guidelines on the basis of a large study by Moon [17], which revealed that PTC lesions often lack blood flow, thus questioning the diagnostic value of color Doppler flow imaging. However, Chammas [18] argued that shortcomings in Moon’s research methods might have underestimated the effectiveness of color Doppler flow imaging. In our study, we employed fine-flow imaging technology with settings of a low-wall filter, a Doppler gain of 80–90, and a velocity scale of 1.5–2.5 cm/s. During the procedure, we ensured that minimal pressure was applied to the probe, as even slight pressure can obscure low-velocity blood flow. The blood flow detection rate was 73.9% (212/287), which demonstrates that, under appropriate parameter settings and when performed by experienced operators, fine-flow imaging can reliably assess the blood flow characteristics of PTC lesions.

The Adler blood flow classification system, originally designed to assess blood flow within breast tissue lesions, aims to quantify blood supply. Although this system was not specifically developed for thyroid diseases, many researchers still use it to describe blood flow characteristics in thyroid cancer patients. In our study, we applied the Adler classification to CDUS images of 287 PTC patients. The results revealed that 139 lesions (48.43%) presented a rich blood supply, whereas 148 lesions (51.56%) presented a poor blood supply. The univariate analysis revealed no significant correlation between Adler’s blood flow grade and LNM in PTC patients (P > 0.05). These findings suggest that the Adler classification system does not adequately reflect the blood flow characteristics of PTC lesions and has limited clinical value in the assessment of PTC. In 1993, Lagalla [8] proposed the following blood flow classification of thyroid nodules on the basis of the spatial distribution of blood vessels: no appreciable glandular flow (I); presence of signals in perinodular locations (II); and perinodular and intranodular flow (III). Chammas [19] and many other scholars later refined the classification to include vascularity. Most current systems used to describe blood flow in thyroid cancer are essentially variations of Lagalla’s original framework, and significant differences in results have been reported across studies. As a result, these existing classification systems have not been formally incorporated into clinical practice guidelines. The lack of consensus and standardization in PTC blood flow classification has hampered the widespread clinical application of these systems. With advancements in research, some scholars have proposed characteristic blood flow patterns in PTC lesions. Xue N [10] noted that the wheel sign on CDUS is highly specific for PTCs. Lacout A [11] observed that penetrating “sword-like” vessels observed on CDUS images may be present in poorly differentiated papillae at an early stage. These studies suggest that PTC exhibits distinct characteristics on CDUS images. However, our understanding of the blood flow characteristics of PTC is still incomplete. The color Doppler imaging of PTCs is complex and varied, but certain patterns are observed. On the basis of extensive clinical observations and analysis of the images collected in this study, we propose a new blood flow classification system (Fig. 2). Unlike previous systems, the new classification system more comprehensively depicts the characteristics of PTC on CDUS. We emphasize the integration of blood flow morphology, and thus it is easier for observers to remember and clearly distinguish different patterns. Notably, the five distinct patterns reflect different aspects of tumor neovascularization and provide an important foundation for further evaluating the relationship between blood flow features and LNM risk.

Color Doppler flow imaging can reveal the characteristics of the internal circulatory system of PTC lesions in different pathological environments. A significant correlation was revealed between the vascular structure of papillary carcinoma and the characteristics of color Doppler images [20]. The rich-disorganized pattern is characterized by abnormal anastomosis of rich blood vessels and the irregular aggregation of locally twisted small blood vessels into “glomerular” structures, which may be the product of tumor neovascularization, while distorted blood vessels may be caused by tumor cell proliferation and extrusion [21, 22]. The branching pattern is characterized by a large vegetative vessel on the outside that penetrates the lesion, which may be related to tumor-host vascular co-selection or angiogenesis. Perforation throughout the tubercle indicates poorly differentiated papillary or anaplastic thyroid nodules at an early stage [11]. The garland pattern is characterized by dense and small perforating branches at the edges, which may be the result of angiogenesis in thyroid cancer, rather than the proliferation of fibrous tissue or scar formation within the tumor that pulls the surrounding host blood vessels [23]. Some scholars have reported that typical PTC and high-cell PTC are characterized by intratumoral blood vessels that connect to form several anastomoses, that sclerotic PTC is poorly vascularized, and that follicular PTC is characterized by small intratumoral branches, frequent anastomosis, and a lack of central large blood vessels [24]. These findings suggest that CDUS blood flow characteristics may be helpful in the differentiation of thyroid papillary carcinoma subtypes. Nevertheless, as angiogenesis is a multifaceted process governed by various cellular factors, the vascular characteristics observed on CDUS images likely arise from a combination of factors. Consequently, obtaining compelling evidence to elucidate the molecular mechanisms underlying blood supply characteristics necessitates more intricate experimental studies.

Angiogenesis and the proliferation of abnormal blood vessels are frequently associated with tumor cell proliferation [25]. In this study, different blood flow patterns were observed in lesions of different sizes (Table 2). Specifically, larger lesions tended to exhibit more abundant and disorganized internal blood flow. This finding aligns with that of Fukuoka et al., who reported that highly vascularized lesions in PTC patients grow significantly faster than those with less vascularization [26]. This consistency underscores the relationship between blood flow characteristics and tumor growth, which reinforces the clinical relevance of evaluating blood flow patterns for the prediction of PTC aggressiveness. A study involving 748 patients demonstrated that primary tumor size is an independent risk factor for cervical LNM [27]. Our study also confirmed that lesion size is an independent risk factor for LNM. However, the univariate analysis revealed no significant correlation between a disorganized blood flow pattern and LNM (P > 0.05), possibly because of the common occurrence of lymphatic invasion in PTC [28]. In contrast, a significant association was observed between the garland pattern and LNM (P < 0.05). Previous research has identified radiating blood flow as a novel ultrasonographic feature of thyroid cancer [29], and the garland pattern observed in our study is highly consistent with one of the radiating blood flow characteristics described in that study. Given the significant correlation between the garland blood flow pattern and LNM, we further performed a multivariate logistic regression analysis. The results indicated that the garland blood flow pattern is an independent protective factor for LNM (OR [95% CI] = 0.386 [0.156–0.893]), which suggests its potential role in predicting LNM in PTC. To validate its predictive value, we integrated this pattern into a regression prediction model. ROC curve analysis demonstrated that Model 1 (AUC = 0.767) had superior discriminative ability in predicting LNM compared with Model 2 (AUC = 0.727). The DeLong test confirmed that the inclusion of the garland blood flow pattern significantly improved the model’s performance, which highlights its unique value in LNM prediction. Figure 7 visualizes the regression system of Model 1 as shown in Table 4. These findings indicate that younger age, larger tumor size, nongarland blood flow, and multifocality are high-risk factors for LNM. Clinicians can utilize this model by assessing relevant high-risk factors in patients to calculate a risk score and estimate the probability of LNM. Angiogenesis is a crucial component of the tumor ecosystem and plays a significant role in tumor biology. In this study, the 33 PTC lesions with garland blood flow exhibited minimal internal vascularization, with a maximum diameter of 19 mm. Previous studies have suggested that tumors lacking a sufficient vascular supply typically do not exceed 20 mm in diameter [30]. Without continuous recruitment of new capillaries, tumors are generally unable to grow beyond a microscopic size of 1–2 mm³ [31]. While insufficient intratumoral vascularization may explain the restricted tumor growth, it does not fully account for the lower risk of LNM. Currently, the underlying mechanism of garland blood flow formation remains unclear. Future studies may clarify this mechanism and potentially lead to important discoveries.

Xiaohong Jia et al. developed a human artificial intelligence hybrid (HAIbrid) framework for thyroid nodule malignancy stratification and diagnosis and identified a second-order interactive feature—vertically distributed blood flow signals surrounding the nodule—that has been overlooked by radiologists and traditional feature selection methods [32]. This feature closely resembles the garland pattern observed in our study. Its incorporation significantly improved the model’s AUC, which demonstrates the potential of pattern-based classification methods in medical imaging. Similarly, research by Burak Taşcı confirmed that pattern-based classification enhances diagnostic accuracy and efficiency because of the automatic extraction and analysis of critical imaging features [33]. The garland pattern proposed in our study, as a pattern-based classification feature, holds significant potential for predicting LNM in PTC. The integration of artificial intelligence techniques could further optimize predictive models and provide robust support for clinical diagnosis.

While the findings of this study are encouraging, several limitations should be acknowledged. First, as a single-center, retrospective study, selection bias is unavoidable. While the sample size is sufficient to support our analysis, the generalizability of the results requires further validation through multicenter studies with larger cohorts. Second, the quality of ultrasound images is influenced by the operator’s skill and experience, and blood flow classification relies on subjective interpretation. The integration of advanced technologies, such as artificial intelligence and machine learning models, may help overcome these limitations.

Conclusion

In conclusion, PTC lesions present certain characteristics on CDUS images, and blood flow can be divided into five patterns: avascular, dot-line, branching, garland, and rich-disorganized. The discovery of the garland pattern provides a novel tool for predicting LNM risk, aiding clinicians in making more accurate assessments of tumor aggressiveness. Further exploration and research on the blood flow characteristics of PTC will provide crucial imaging insights to support personalized treatment and follow-up management.