Development and validation of a preoperative prediction model for follicular thyroid carcinoma

Intra- and Peritumoral Radiomics Based on Ultrasound Images for Preoperative Differentiation of Follicular Thyroid Adenoma, Carcinoma, and Follicular Tumor With Uncertain Malignant Potential

OBJECTIVE

Differentiating between follicular thyroid adenoma (FTA), carcinoma (FTC), and follicular tumor with uncertain malignant potential (FT-UMP) remains challenging due to their overlapping ultrasound characteristics. This retrospective study aimed to enhance preoperative diagnostic accuracy by utilizing intra- and peritumoral radiomics based on ultrasound images.

METHODS

We collected post-thyroidectomy ultrasound images from 774 patients diagnosed with FTA (n = 429), FTC (n = 158), or FT-UMP (n = 187) between January 2018 and December 2023. Six peritumoral regions were expanded by 5%-30% in 5% increments, with the segment-anything model utilizing prompt learning to detect the field of view and constrain the expanded boundaries. A stepwise classification strategy addressing three tasks was implemented: distinguishing FTA from the other types (task 1), differentiating FTC from FT-UMP (task 2), and classifying all three tumors. Diagnostic models were developed by combining radiomic features from tumor and peritumoral regions with clinical characteristics.

RESULTS

Clinical characteristics combined with intratumoral and 5% peritumoral radiomic features performed best across all tasks (Test set: area under the curves, 0.93 for task 1 and 0.90 for task 2; diagnostic accuracy, 79.9%). The DeLong test indicated that all peritumoral radiomics significantly improved intratumoral radiomics performance and clinical characteristics (p < 0.04). The 5% peritumoral regions showed the best performance, though not all results were significant (p = 0.01-0.91).

CONCLUSION

Ultrasound-based intratumoral and peritumoral radiomics can significantly enhance preoperative diagnostic accuracy for FTA, FTC, and FT-UMP, leading to improved treatment strategies and patient outcomes. Furthermore, the 5% peritumoral area may indicate regions of potential tumor invasion requiring further investigation.

Establish a Nomogram Based on Ultrasound Novel Vascularity Characteristics to Differentiate Follicular Thyroid Carcinoma From Follicular Benign Neoplasms

PURPOSE

This study aimed to construct a nomogram based on novel ultrasound vascularity features to differentiate follicular thyroid carcinoma (FTC) from follicular benign neoplasms (FBN).

METHODS

A total of 153 patients were included. We evaluated various sonographic features of all nodules and analyzed the diagnostic efficiency of different vascularity patterns for FTC. Furthermore, independent predictors for FTC were screened using multivariable logistic regression. A nomogram was established to predict FTC, and various methods were used to evaluate the performance of the nomogram.

RESULTS

The firework-like pattern and central-grasp pattern were more likely to appear in the FTC (p < 0.001, p = 0.007), while the ring-like pattern was more likely to appear in the FBN (p < 0.001). The firework-like pattern showed the best performance in accuracy (71.2%) and NPV (73.4%) among other vascularity patterns. Besides, thick hypo-halo, ring-like pattern, central-grasp pattern, and firework-like pattern were four predictors used to construct the nomogram. The bootstrap AUC was 0.814 (95% CI: 0.743-0.884) which revealed the nomogram had accurate prediction accuracy.

CONCLUSION

The firework-like pattern and central-grasp pattern were vascularity characteristics for FTC, while the ring-like pattern was for FBN. Besides, our nomogram based on novel vascularity characteristics can help distinguish FTC and FBN before operation.

Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm

OBJECTIVES

To evaluate the value of radiomics based on ultrasonography in differentiating follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) and construct a tool for preoperative noninvasive predicting FTC and FTA.

METHODS

The clinical data and ultrasound images of 389 patients diagnosed with FTC or FTA postoperatively were retrospectively analyzed at 3 institutions from January 2017 to December 2023. Patients in our hospital were randomly assigned in a 7:3 ratio to training cohort and validation cohort. External test cohort consisted of data collected from other 2 hospitals. Radiomics features were used to develop models based on different machine learning classifiers. A combined model was developed combining radiomics features with clinical characteristics and a nomogram was depicted. The performance of the models was assessed by area under the receiver operating characteristic curve (AUC), calibration curve and decision curve.

RESULTS

Radiomics model based on random forest showed best performance in discriminating FTC and FTA, with AUCs 0.880 (95% confidence interval [CI]: 0.8290-0.9308), 0.871 (95% CI: 0.7690-0.9734), and 0.821 (95% CI: 0.7036-0.9389) in training, validation, and test cohort, respectively. The combined model presented better efficacy comparing with clinical model and radiomics model, with AUCs 0.883 (95% CI: 0.8359-0.9295), 0.874 (95% CI: 0.7873-0.9615), and 0.876 (0.7809-0.9714) in training, validation, and test cohort, respectively. The calibration curves suggested good consistency and decision curves showed the highest overall clinical benefit for the combined model.

CONCLUSIONS

Ultrasound radiomics model based on random forest is feasible to differentiate FTC and FTA, and the combined model is an intuitively noninvasive tool for FTC and FTA preoperative identification.

Development and validation of a predictive model for assessing the risk of follicular carcinoma in thyroid nodules identified as suspicious by intraoperative frozen section

Introduction

Follicular thyroid carcinoma (FTC) is the second most common thyroid malignancy and is characterized by a higher risk of distant metastasis compared to papillary thyroid cancer. Intraoperative frozen section (IOFS) diagnosis of FTC is challenging due to its limited sensitivity and accuracy, leading to uncertainty in intraoperative surgical decision-making. In response, we developed a predictive model to assess the risk of follicular carcinoma in thyroid nodules identified as suspicious for follicular neoplasm by IOFS.

Methods

This model was derived from preoperative clinical and ultrasound data of 493 patients who underwent thyroid surgery at Ningbo Medical Center Lihuili Hospital. It identified five significant predictors of follicular carcinoma: nodule size, thyroglobulin (Tg) level, hypoechogenicity, lobulated or irregular margins, and thick halo.

Results

The model demonstrated robust discrimination and calibration, with an area under the curve (AUC) of 0.83 (95% CI: 0.77-0.90) in the training set and 0.78 (95% CI: 0.68-0.88) in the validation set. In addition, it achieved a sensitivity of 81.63% (95% CI: 69.39-91.84) and 68.00% (95% CI: 48.00--4.00), a specificity of 77.42% (95% CI: 72.18-82.66) and 72.51% (95% CI: 65.50-78.96), an accuracy of 78.1% (95% CI: 73.4-82.4) and 71.9% (95% CI: 65.3-78.6), a positive predictive value (PPV) of 41. 67% (95% CI: 35.65-48.84) and 26.79% (95% CI: 19.40-34.33), respectively, and a negative predictive value (NPV) of 95.61% (95% CI: 92.86-97.99) and 94.07% (95% CI: 90.44-97.08) in the training and validation sets, respectively.

Conclusion

The model can accurately rule out FTC in low-risk nodules, thereby providing surgeons with a practical tool to determine the necessary extent of surgical intervention for nodules flagged as suspicious by IOFS.

US Risk Stratification System for Follicular Thyroid Neoplasms

Background Preoperative assessment of follicular thyroid neoplasms is challenging using the current US risk stratification systems (RSSs) that are applicable to papillary thyroid neoplasms. Purpose To develop a US feature-based RSS for differentiating between follicular thyroid adenoma (FTA) and follicular thyroid carcinoma (FTC) in biopsy-proven follicular neoplasm and compare it with existing RSSs. Materials and Methods This retrospective multicenter study included consecutive adult patients who underwent conventional US and received a final diagnosis of follicular thyroid neoplasm from seven centers between January 2018 and December 2022. US images from a pretraining data set were used to improve readers' understanding of the US characteristics of the FTC and FTA. Univariable and multivariable logistic regression analyses were used to assess the association of qualitative US features with FTC in a training data set. Features with P < .05 were used to construct a prediction model (follicular tumor model, referred to as F model) and RSS for follicular neoplasms using the Thyroid Imaging Reporting and Data System (TI-RADS). Area under the receiver operating characteristic curve (AUC) was compared between follicular TI-RADS (hereafter, F-TI-RADS) and existing RSS (American College of Radiology [ACR] TI-RADS, Korean Society of Thyroid Radiology and Korean Society of Radiology TI-RADS [hereafter, referred to as K-TI-RADS], and Chinese TI-RADS [hereafter, referred to as C-TI-RADS]) in a validation data set. Results The pretraining, training, and validation data sets included 30 (mean age, 47.6 years ± 16.0 [SD]; 16 male patients; FTCs, 30 of 60 [50.0%]), 703 (mean age, 47.9 years ± 14.5; 530 female patients; FTCs, 188 of 703 [26.7%]), and 155 (mean age, 49.9 years ± 13.3 [SD]; 155 female patients; FTCs, 43 of 155 [27.7%]) patients. In the validation data set, the F-TI-RADS showed improved performance for differentiating between FTA and FTC (AUC, 0.81; 95% CI: 0.71, 0.86) compared with ACR TI-RADS (AUC, 0.74; 95% CI: 0.66, 0.80; P = .02), K-TI-RADS (AUC, 0.69; 95% CI: 0.61, 0.76; P = .002), and C-TI-RADS (AUC, 0.68; 95% CI: 0.60, 0.75; P = .002). Conclusion F-TI-RADS outperformed existing RSSs for differentiating between FTC and FTA. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Baumgarten in this issue.

The correlation between preoperative serum thyroglobulin and pathological features of thyroid follicular carcinoma.. [DOI: 10.21203/rs.3.rs-1965404/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Purpose: To explore more meaningful information for making individualized medical decisions for patients with follicular thyroid carcinoma (FTC), we investigated the correlations between pathological features of FTC and preoperative serum markers, including thyroglobulin (Tg), anti-thyroglobulin antibody (TgAb), thyroid peroxidase antibodies (TPOAb), thyroid stimulating hormone (TSH), free triiodothyronine (fT3), free thyroxine (fT4), and fT3/fT4 ratio.Methods:We retrospectively analyzed 82 patients with FTC. Data collected included demographics (sex and age), pathological features (infiltration extent, tumor stage, presence of multifocal FTC, and presence of concomitant PTC), and preoperative serum markers (Tg, TgAb, TPOAb, TSH, fT3, fT4, and fT3/fT4 ratio). The correlations between preoperative serum markers and pathological features were analyzed.Results:72.0% patients were female. Mean age was 46.4±16.5 years. In univariate analysis, older age (≥55 years) was associated with non-minimally invasive (P=0.016), larger (>T2 category, P=0.006), and multifocal (P=0.034) FTC; elevated preoperative serum Tg level (≥500.00 ng/mL) was associated with non-minimally invasive (P=0.015) and larger (>T2 category, P=0.001) FTC; higher fT3/fT4 ratio (≥0.541) was associated with multifocal FTC (P=0.031). In multivariable analysis, Tg ≥500.00 ng/mL was an independent risk factor for non-minimally invasive FTC [P=0.015, OR=3.289 (1.260-8.583)] and for >T2 category FTC [P=0.001, OR=5.397 (1.963-14.840)]; older age [≥55 years, P=0.045, OR=4.756 (1.037-21.818)] and higher fT3\fT4 ratio [≥0.541, P=0.044, OR=4.626 (1.043-20.525)] was an independent risk factor multifocal FTC, respectively.Conclusions: Preoperative serum Tg was correlated with the local tumor extent and primary tumor diameter of FTC. Further research regarding the utility of preoperative serum Tg in FTC is still needed.