Development of a 18F-FDG PET/CT-based Radiomics Model for Predicting Axillary Lymph Node Metastasis in Breast Cancer

Development of a 18F-FDG PET/CT-based Radiomics Model for Predicting Axillary Lymph Node Metastasis in Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development of a 18 F-FDG PET/CT-based Radiomics Model for Predicting Axillary Lymph Node Metastasis in Breast Cancer Zhen Yu, Ke Dong, Qifeng Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5297857/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Axillary lymph node metastasis (ALNM) status is an important factor for the determination of the therapeutic strategies and breast cancer prognosis. In our study, we investigate whether radiomics features from 18 F-fluorodeoxyglucose( 18 F-FDG) positron emission tomography /computed tomography (PET/CT), combined with clinical or pathological characteristics, provide a higher predictive value of ALNM. Methods A retrospective analysis was performed on 78 female patients who underwent preoperative 18 F-FDG PET/CT scans at Jinhua Central Hospital from August 2015 to July 2024, with a mean age of 53.60 ± 12.49 years (range: 35–84 years). The cases were randomly divided into a training cohort (46 cases) and a testing cohort (32 cases) in a 6:4 ratio. All patients' PET/CT and clinical pathological features were analyzed, and radiomics features were extracted from the PET/CT images. Subsequently, we developed radiomics, clinical, and combined radiomics-clinical models. We also assessed the performance of these three models in predicting ALNM. The Python stats models package (version 0.13.2) was used for statistical analysis. Results For the three features radiomics model and combined model in the training cohort, the area under the curve (AUC) was 0.922 and 0.931, which were both higher than that of the traditional clinical feature model (AUC = 0.917). The AUC values for the three models in the testing cohort were 0.802, 0.821, and 0.778. For predicting ALNM across all cohorts, the radiomics model and the combined model showed clinical benefit in the decision curve analysis (DCA). Conclusion The PET/CT-based radiomics model demonstrated strong efficacy in predicting ALNM for breast cancer and has clinical application value. Breast cancer PET/CT Radiomics Axillary lymph node metastasis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Breast cancer is one of the leading causes of cancer-related mortality and the most common cancer among female patients. According to global cancer statistics, its incidence among female patients is higher than the incidence of lung cancer[ 1 ]. Breast cancer’s most common metastatic pathway is ALNM. For example, the 5-year survival rate is 98.8%, for patients with localized lesions confined to the breast, while it decreases to 85.8% for those with ALNM [ 2 ]. The treatment plan depends on the presence or absence of ALNM. Currently, ALNM is determined by sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND). Both procedures are invasive and may result in various adverse effects, like infection, sensory abnormalities, upper limb mobility issues, and lymphedema, while also possessing a certain false-negative rate. However, compared to ALND, SLNB is less invasive and has a lower incidence of adverse effects, and it has gradually replaced ALND in clinical practice[ 3 ]. The treatment guidelines from the American Medical Association also recommend that no further ALND is necessary when the SLNB result is negative[ 4 ]. Although SLNB has become the primary approach for clinically assessing ALNM, studies have shown that the negative rate of SLNB can reach 70%, with a complication rate of 41% post-SLNB[ 5 ]. Therefore, non-invasive evaluation of ALNM before treatment holds significant clinical importance. The preoperative assessment of ALNM in clinical practice primarily relies on imaging examinations, including breast ultrasound, mammography, etc[ 6 ]. However, the sensitivity of these examinations for ALNM remains relatively low. In contrast to other imaging modalities, PET/CT combines the high-resolution anatomical localization of CT with the high sensitivity and specificity of metabolic imaging from PET, making it widely used for non-invasive diagnosis at the molecular imaging level[ 7 ]. Currently, nuclear medicine physicians commonly use maximum standardized uptake value (SUVmax) as a critical criterion for assessing the malignancy of lesions, with higher SUVmax values generally indicating a greater likelihood of malignancy. This assessment is often supplemented by other imaging characteristics of the lesions, such as morphology, volume, and density[ 7 ]. Numerous studies have shown a correlation between SUVmax and tumor malignancy[ 8 ][ 9 ]. However, the accuracy of these assessments can be significantly affected by image quality and subjective factors. In cases where lesions are small (< 0.5 cm), exhibit high differentiation, or grow slowly, the likelihood of false negatives increases. Radiomics is a rapidly evolving research field that extracts a vast array of radiomics features from high-throughput imaging data. It offers richer imaging information, thereby reducing the limitations of subjective interpretation by physicians and enhancing the quality of medical imaging diagnosis. Research based on PET/CT radiomics is still scarce, despite the fact that several studies have used ultrasonography, CT, and MRI radiomics to predict ALNM in breast cancer[ 10 ][ 11 ]. The purpose of this study is to investigate the predictive usefulness of preoperative 18 F-FDG PET/CT radiomics for breast cancer ALNM, offering more data to improve clinical diagnosis and guide treatment decisions. Methods Patient Since this is a retrospective analysis, Jinhua Central Hospital's Ethics Committee, which is connected to Zhejiang University, has waived the need for patient-informed consent. A retrospective collection of pre-treatment 18 F-FDG PET/CT imaging data, along with clinical and pathological data, was conducted for breast cancer patients confirmed by pathology at Jinhua Hospital affiliated with Zhejiang University, covering the period from August 5, 2015, to July 25, 2024. Data were rigorously screened according to inclusion and exclusion criteria. The patient selection process is illustrated in Fig. 1 . Inclusion criteria: 1) No treatment prior to PET/CT examination, including surgery, radiotherapy, chemotherapy, and immunotherapy; 2) Both the primary breast cancer lesion and ALNM lesions were confirmed by pathology; 3) Complete clinical and pathological information. Exclusion criteria: 1) Individuals who were on anticancer medication prior to PET/CT scanning; 2) Inadequate clinical and pathological information; 3) Existence of more cancerous tumors; 4) Poor image quality of PET/CT, affecting interpretation of results. A training cohort and a testing cohort were randomly selected from the dataset in a 6:4 ratio. Instances from the testing cohort were used for an impartial assessment of the prediction model's performance, while all instances from the training cohort were used to train the model. PET/CT Scanning Technology All enrolled patients underwent scanning using the Siemens PET/CT-Biograph mCT. Prior to the examination, patients were told to fast for 6–8 hours, and their height, weight, and blood glucose levels were measured. An intravenous injection of the 18 F-FDG imaging agent was administered at a dose of 0.1–0.15 mCi/kg, ensuring that the patient's fasting blood glucose level was below 11.1 mmol/L prior to injection. The patient was then instructed to rest quietly for 1 hour before undergoing PET/CT scanning. The following were the CT scan parameters: 120 kV tube voltage and a tube current automatic mAs technology, the slice thickness is 3 mm, pitch is 0.8, and rotation duration is 0.5 s/r. The PET scan parameters were as follows: 1.5 min/bed, with 6 to 8 bed positions, and the slice thickness of 3 mm. For post-processing, the scanned photos are uploaded to the imaging workstation. Image Assessment The images are evaluated by two radiologists at our center, each with over three years of diagnostic experience, who are kept unaware of the patients' clinical information and pathology results. The evaluation criteria are as follows: A higher uptake of the radiopharmaceutical in breast tissue compared to surrounding areas indicates the presence of breast cancer, while an increased uptake in lymph nodes compared to adjacent muscle tissue suggests lymph node metastasis. A semi-automated delineation of the region of interest (ROI) is performed based on a 40% SUVmax threshold, measuring PET metabolic parameters, including SUVmax, minimum standardized uptake value (SUVmin), average standardized uptake value (SUVavg), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Additionally, the maximum diameter of the lesion in the axial plane is measured. In cases with multiple lesions, only the largest lesion by volume is selected for measurement. Radiomics Data Collection: Export the raw DICOM data from the "Medex" platform. Image Segmentation: Using the 3D Slicer software (version 5.6.1), select the "Draw" tool to manually delineate the ROI layer by layer on the PET/CT fused images, taking care to avoid necrotic areas. Perform radiomics feature extraction on the segmented ROI using the "Radiomics" package for both PET and CT images. Figure 2 displays the radiomics analysis workflow used in this investigation. Three groups of radiomics features can be distinguished: (I) geometry, (II) intensity, and (III) texture. Tumors are characterized by geometric features in three dimensions. The first-order statistical distribution of voxel intensities inside the tumor is described by intensity characteristics. The higher-order spatial distribution of the intensity patterns is described by the texture features. Z-score normalization is used to address the issue of scale variation in manually extracted radiomics features. Using a double-blind technique, two nuclear medicine doctors separately segmented each lesion. The intraclass correlation coefficient (ICC) was used to assess the stability of feature extraction; an ICC > 0.75 indicates strong consistency in feature extraction. Clinical and Pathological Characteristics Clinical information were recorded for all included patients, including age, body mass index (BMI), menstrual status, tumor T stage, carcinoembryonic antigen (CEA) levels, carbohydrate antigen 125 (CA125) levels, and carbohydrate antigen 153 (CA153) levels. Additionally, the following pathological and immunohistochemical data were recorded: estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER-2) status, tumor cell proliferation marker 67 (Ki-67) levels, and molecular subtypes of breast cancer. ER positivity is defined as ≥ 10% of tumor cell nuclei staining positive; PR positivity is defined as ≥ 10% of tumor cell nuclei staining positive, with ≥ 20% indicating high expression, and lower percentages indicating low expression; HER-2 positivity is defined as an immunohistochemical score of ≥ 2 + or positive FISH amplification; Ki-67 expression index ≥ 14% indicates high expression, while lower percentages indicate low expression. According to the 2023 NCCN guidelines[ 12 ], breast cancer molecular subtyping is determined based on immunohistochemical results, specifically: luminal A, luminal B, HER-2 positive, and triple-negative. Feature Selection and Model Construction A Mann-Whitney U test was run on all radiomics characteristics, preserving only those with a p-value < 0.05. The correlation between characteristics with high redundancy was determined using Pearson's rank correlation coefficient, which kept features with a correlation coefficient of at least 0.9 between any two features. For additional feature selection, the Least Absolute Shrinkage and Selection Operator (LASSO) was utilized. To find the ideal λ, 10-fold cross-validation was utilized, keeping features with non-zero coefficients. Finally, the Rad score was calculated by summing the products of the LASSO-Logistic regression coefficients and their corresponding values. This study also compared the stability and reliability of seven machine learning algorithms in predicting ALNM, including Logistic Regression (LR), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest (RF), Extra Trees, and Extreme Gradient Boosting (XGBoost). The diagnostic performance of the optimal predictive model was evaluated using the Receiver Operating Characteristic (ROC) curve. Building the clinical model follows a nearly identical procedure to that of building the radiomics model. First, statistical variables with a p-value < 0.05 were selected from the clinical baseline data, and a clinical model was constructed using the optimal machine learning algorithm, incorporating 5-fold cross-validation and a fixed testing cohort. Finally, a nomogram was constructed based on logistic regression analysis by integrating radiomics features and clinical characteristics, and the ROC curve and calibration curve were plotted to evaluate its performance. The clinical utility was evaluated using DCA. Statistical Analysis The Python statsmodels module (version 0.13.2) was utilized for statistical analysis, and a p-value < 0.05 was considered statistically significant. For continuous variables, the Mann-Whitney U test was utilized to examine intergroup differences; for categorical variables, the χ2 test was employed. Results Clinical Feature This study included a total of 78 pathologically confirmed female breast cancer patients, with an average age of 53.60 ± 12.49 year. Among them, 57 patients had positive ALNM, while 21 had negative results. The subjects were randomly allocated into a training cohort (46 instances) and a testing group (32 cases) in a 6:3 ratio. The training cohort included 34 cases with positive ALNM and 12 cases with negative results, while the testing cohort comprised 23 positive cases and 9 negative cases. The participants in our cohort's baseline clinical characteristics are displayed in Table. 1 . Age, BMI, tumor markers, MTV, TLG, menstrual status, and immunohistochemical indicators showed no significant differences between two cohorts (P > 0.05), while tumor diameter, SUVmax, SUVmin, SUVavg, T staging, and breast cancer molecular subtypes exhibited significant differences (P < 0.05). Table 1 Characteristics of patients in our cohorts. Features ALL(n = 87) Non-Metastasis(n = 30) Metastasis(n = 57) P -value Age 53.60 ± 12.49 56.52 ± 16.22 52.53 ± 10.78 0.379 BMI 22.57 ± 3.59 22.81 ± 4.25 22.48 ± 3.36 0.892 Tumor diameter 31.09 ± 19.11 21.01 ± 16.66 34.80 ± 18.73 < 0.001 Menstrual status 0.891 0 40(51.28) 10(47.62) 30(52.63) 1 38(48.72) 11(52.38) 27(47.37) T stage < 0.001 1 18(23.08) 14(66.67) 4(7.02) 2 41(52.56) 6(28.57) 35(61.40) 3 11(14.10) Null 11(19.30) 4 8(10.26) 1(4.76) 7(12.28) CEA 37.08 ± 100.61 18.93 ± 44.65 43.76 ± 114.18 0.461 CA125 92.73 ± 301.16 95.99 ± 362.67 91.54 ± 278.81 0.161 CA153 26.70 ± 53.65 15.02 ± 22.34 31.00 ± 60.90 0.226 ER 1 0 24(30.77) 6(28.57) 18(31.58) 1 54(69.23) 15(71.43) 39(68.42) PR 0.751 0 45(57.69) 11(52.38) 34(59.65) 1 33(42.31) 10(47.62) 23(40.35) HER2 0.983 0 24(30.77) 7(33.33) 17(29.82) 1 54(69.23) 14(66.67) 40(70.18) Ki67 1 0 17(21.79) 5(23.81) 12(21.05) 1 61(78.21) 16(76.19) 45(78.95) Mol-subtypes 0.27 1 2(2.56) Null 2(3.51) 2 17(21.79) 4(19.05) 13(22.81) 3 35(44.87) 11(52.38) 24(42.11) 4 19(24.36) 3(14.29) 16(28.07) 5 5(6.41) 3(14.29) 2(3.51) SUVmax 10.17 ± 6.12 6.82 ± 4.35 11.41 ± 6.25 0.001 SUVmin 4.32 ± 2.44 2.43 ± 1.54 5.02 ± 2.34 < 0.001 SUVavg 6.19 ± 3.70 4.24 ± 2.68 6.91 ± 3.79 0.003 MTV 16.46 ± 37.50 5.98 ± 6.97 20.32 ± 43.12 0.032 TLG 147.58 ± 443.00 31.20 ± 44.44 190.46 ± 512.04 0.004 Radiomics Features Selection and Model Establishment Using the 3D Slicer program, 1,702 radiomics characteristics in total were retrieved. There were 246 features left after all features were first screened using the Mann-Whitney U test. Following that, a Pearson correlation analysis was carried out, yielding 65 residual characteristics. Finally, LASSO regression with an optimal λ of 0.1048 identified three non-zero coefficients for the radiomics features, establishing the Rad score(Fig. 3 ). The following formula is used to determine the Rad score: 0.739130434782608 + 0.02911 * wavelet_LHL_glszm_SizeZoneNonUniformityNormalized_CT + 0.047979 * wavelet_LLL_glcm_SumAverage_CT − 0.107920 * wavelet_HHL_glcm_JointAverage_PET This study compared predictive models established using seven different machine learning algorithms. The results indicated that the radiological model constructed using the LR method exhibited the best stability and reliability ( Table. 2 ). Ultimately, the radiomics model based on LR was selected to develop the subsequent nomogram. Table 2 Performance of different machine learning models in predicting ALNM in both cohorts. Model Accuracy AUC 95%CI Sen Spe PPV NPV LR-train 0.891 0.922 0.8347–1.0000 0.912 0.833 0.939 0.769 LR-test 0.719 0.802 0.6497–0.9542 0.609 1 1 0.5 Naïve Bayes-train 0.87 0.892 0.7840–1.0000 0.882 0.833 0.937 0.714 Naïve Bayes-test 0.688 0.787 0.6298–0.9451 0.565 1 1 0.474 KNN-train 0.761 0.93 0.8611–0.9992 0.676 1 1 0.522 KNN-test 0.688 0.72 0.5164–0.9232 0.696 0.667 0.842 0.462 Decision Tree-train 0.261 0.985 0.9650–1.0000 0 1 0 0.261 Decision Tree-test 0.281 0.684 0.4892–0.8779 0 1 0 0.281 Random Forest-train 0.913 0.988 0.9660–1.0000 0.882 1 1 0.75 Random Forest-test 0.531 0.804 0.6539–0.9548 0.348 1 1 0.375 Extra Trees-train 0.935 0.98 0.9489–1.0000 0.941 0.917 0.97 0.846 Extra Trees-test 0.656 0.751 0.5824–0.9200 0.522 1 1 0.45 XG Boost-train 0.978 1 1.0000–1.0000 0.971 1 1 0.923 XG Boost-test 0.656 0.645 0.4072–0.8826 0.609 0.778 0.875 0.437 Clinical Features Selection and Model Establishment A predictive model was developed using the clinical features of patients in the training cohort, with the univariate and multivariate analysis results for 46 breast cancer patients presented in Table. 3. The results of the univariate logistic regression analysis indicated that tumor diameter, SUVmax, SUVmin, SUVavg, and tumor T stage are risk factors for ALNM. The creation of the clinical model took into account the findings of further multivariate logistic regression analysis, which showed that SUVmin (OR = 1.175, P < 0.05) and tumor T stage (OR = 1.246, P < 0.05) are independent risk factors for ALNM. Table 3 Logistic regression analysis of clinical data in the training cohort. Feature Univariate analysis Multivariate analysis OR (95% CI) P -value OR (95% CI) P -value Tumor diameter 1.010(1.004–1.016) 0.009 1.004(0.989–1.018) 0.67 T stage 1.317(1.188–1.461) 0 1.246(1.051–1.477) 0.035 SUVmax 1.022(1.005–1.049) 0.035 3.026(0.355–25.842) 0.389 SUVavg 1.034(1.006–1.062) 0.043 0.569(0.14–2.316) 0.502 SUVmin 1.083(1.043–1.124) 0.001 1.175(1.06–1.303) 0.012 Predictive Performance of Different Models Three models were used to assess the prediction accuracy for ALNM: the clinical model, the radiomics model, and the combination model. The training cohort's AUCs were 0.917, 0.922, and 0.931, while the testing cohorts were 0.778, 0.802, and 0.821 (Fig. 4 ). In both cohorts, the combined model's AUC was found to be significantly higher by the DeLong test than by the clinical and radiomics models ( Table. 4 ). Ultimately, a nomogram was created by merging the extremely stable and dependable Rad score derived from logistic regression with clinical prognostic criteria (Fig. 5 ). The calibration curve showed that the combined model fit well to forecast the spread of breast cancer to the axillary lymph nodes (Fig. 6 ). Furthermore, the DCA showed that there is a substantial net advantage offered by the combined approach (Fig. 7 ). Table 4 Delong test. Cohort Combined Vs Clinic Combined Vs Rad Rad Vs Clinic P -value Train 0.016 0.027 0.927 P -value Test 0.027 0.026 0.831 Discussion As one of the most prevalent cancers in females, breast cancer affects a younger and younger population globally each year in terms of incidence [ 13 ]. Precise determination of lymph node staging in breast cancer is essential for choosing therapeutic treatment strategies and forms a significant foundation for prognostic assessment. The primary method for assessing ALNM in breast cancer is SLNB; however, this procedure may result in postoperative complications and impact recovery. A non-invasive method for forecasting the metastases of axillary lymph nodes is provided by radiomics. Radiomics provides a non-invasive means of predicting metastases from axillary lymph nodes by supplying features from images that are not visible through conventional imaging assessment methods. These features are often linked to genomic, cellular, and metabolic information relevant to tumor biology[ 14 , 15 ]. Some studies have utilized MRI radiomics to predict pelvic lymph node metastasis in cervical cancer, reporting AUC values of 0.82, 0.84, and 0.9 [ 16 – 18 ]. A meta-analysis using ultrasound radiomics has also been used in papers summarizing prognostic research on cervical lymph node metastasis in thyroid cancer, with a sensitivity value of 0.81 (95% CI, 0.73–0.86) and a specificity value of 0.87 (95% CI, 0.83–0.91) [ 19 ]. Zhao et al.'s[ 20 ] study, which had an AUC of 0.91, showed how CT radiomics analysis might improve preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma. The aforementioned studies indicate that conventional radiomics demonstrates a strong predictive capability for lymph node metastasis in tumors. This work developed a hybrid model that uses 18 F-FDG PET/CT radiomics and clinical variables to predict the metastasis of axillary lymph nodes in breast cancer. Independent validation with a testing cohort confirmed the model's strong predictive capability. This study included five metabolic parameters from PET/CT in the clinical characteristics. Statistical analysis indicated that SUVmin demonstrated a strong predictive capability within our cohort. In related studies on PET/CT radiomics, various metabolic parameters have frequently shown significant predictive abilities. For instance, research by Moazemi et al.[ 21 ] revealed that the SUVmin from pre-treatment 68 Ga-PSMA PET/CT may serve as a potential predictor of overall survival in patients with advanced prostate cancer undergoing 177 Lu-PSMA therapy. This may be attributed to the fact that low SUV values can indicate a certain level of tumor heterogeneity, which may be associated with a broader spectrum of activity values within the tumor. This study extracted a total of 1,702 radiomics features through data analysis. After conducting differential analysis and LASSO regression, three radiomics features were selected for model construction. These features are closely related to the regional texture, gray values, contrast, and frequency domain characteristics of breast cancer lesions. They exhibit partial similarity to the radiomics features used in the comprehensive predictive model developed by Li et al.[ 22 ], suggesting that PET/CT imaging manifestations of different primary breast cancer lesions present various subtle differences. The combined model in this investigation did well in terms of predictive power. In the training cohort, the AUC for predicting the metastasis of axillary lymph nodes was 0.931, whereas in the testing cohort, it was 0.821. Prior research has demonstrated that conventional clinical characteristics can forecast the ALNM of breast cancer. However, compared to models relying on SUVmin and tumor T staging clinical indicators, the radiomic model demonstrated higher accuracy in both cohorts. This suggests that radiomics may possess a superior ability to identify tumor heterogeneity in breast cancer. We created a combined predictive model to assess whether clinical factors could improve the radiomics model's performance even further. According to the results, the combined model's prediction capacity outperformed the radiomics model alone in both cohorts, according to the DeLong test. This indicates that the combined model can integrate both types of information, capturing a more comprehensive set of features and thereby improving predictive accuracy. One of the study's limitations is that it is a retrospective study conducted at a single location, which raises the possibility of selection bias. Furthermore, because of the small sample size, it's possible that several indicators that revealed differences in univariate analysis won't reach statistical significance in the cohort's multivariate analysis. Finally, this study did not exclude patients with multifocal breast cancer. Future multicenter, large-sample, prospective studies are expected to improve the treatment of breast cancer by further validating the prognostic usefulness of 18 F-FDG PET/CT imaging for ALNM. Conclusion The multivariable model developed from 18 F-FDG PET/CT radiomics and clinical features demonstrates strong predictive performance for ALNM in breast cancer. This model is expected to serve as a reference for personalized precision treatment decisions in clinical practice. Abbreviations ALNM Axillary lymph node metastasis 18 F-FDG 18 F-fluorodeoxyglucose PET/CT Positron emission tomography /computed tomography AUC Area under the curve DCA Decision curve analysis SLNB Sentinel lymph node biopsy ALND Axillary lymph node dissection ROI Region of interest SUVmax Maximum standardized uptake value SUVmin Minimum standardized uptake value SUVavg Average standardized uptake value MTV Metabolic tumor volume TLG Total lesion glycolysis ICC Intraclass correlation coefficient BMI Body mass index CEA Carcinoembryonic antigen CA125 Carbohydrate antigen 125 CA153 Carbohydrate antigen 153 ER Estrogen receptor PR Progesterone receptor HER-2 Human epidermal growth factor receptor 2 Ki-67 Tumor cell proliferation marker 67 LASSO Least absolute shrinkage and selection operator LR Logistic Regression KNN K-Nearest Neighbors RF Random Forest XGBoost Extreme Gradient Boosting ROC Receiver operating characteristic Declarations Ethical Approval This retrospective study was approved by the Biomedical Ethics Committee of Jin Hua Municipal Central Hospital, which waived the requirement for patient informed consent. Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by Zhejiang Province Public Welfare Technology Application Research Project (LGF21H180007). Author Contribution ZY: Conceptualization, Data curation & analysis, Writing – original draft. KD: Funding acquisition, Resources, Software, Writing – original draft. QH: Project administration, Supervision, Validation, Writing – review & editing. All authors participated in the revision and improvement of the manuscript to ensure the accuracy and completeness of the content. Acknowledgement We thank all the participants and all the researchers and collaborators who participated in this study. References Sung, H, Ferlay, J, Siegel, RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA-CANCER J CLIN. 2021; 71 CA-CANCER J CLIN. 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Radiomics analysis of CT imaging improves preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma. EUR RADIOL. 2023; 33 EUR RADIOL. Moazemi, S, Erle, A, Lütje, S, et al. Estimating the Potential of Radiomics Features and Radiomics Signature from Pretherapeutic PSMA-PET-CT Scans and Clinical Data for Prediction of Overall Survival When Treated with 177Lu-PSMA. Diagnostics (Basel). 2021; 11 (2): Li, Y, Han, D, Shen, C, et al. Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study. BMC Cancer. 2023; 23 BMC Cancer. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5297857","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371291330,"identity":"015bb837-11ed-4235-9f97-063399fdaf73","order_by":0,"name":"Zhen Yu","email":"","orcid":"","institution":"Jin Hua Municipal Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Yu","suffix":""},{"id":371291331,"identity":"7888622b-149a-4ae5-8647-26047ce7b896","order_by":1,"name":"Ke Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYBACxhlAgueADQ8/fwNpWtJkJGccINYaCbCWwzYGDQlE6mCe3WMm8ebMeR4DhgOMHz7mEOOwOWfMJOfcuM1jztzALDlzGzFaZuSYSfN8uM1j2XCAjZmXBC3neAwOJJCk5cYBkrSkFVvOOZPMIznjYDNxfjGckbzxxptjdvb8/M0HP3wkSksDh4kE1MIGItQDgTwD++MPxCkdBaNgFIyCEQsAiB85OzfRWHMAAAAASUVORK5CYII=","orcid":"","institution":"Jin Hua Municipal Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Ke","middleName":"","lastName":"Dong","suffix":""},{"id":371291332,"identity":"3d2127c6-f918-4aed-8f15-b5037f8b5007","order_by":2,"name":"Qifeng Huang","email":"","orcid":"","institution":"Jin Hua Municipal Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qifeng","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-10-20 10:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5297857/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5297857/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68203699,"identity":"9df4169a-35ae-4d07-bf19-113135feb8c4","added_by":"auto","created_at":"2024-11-04 16:00:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":197612,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the patient recruitment process\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5297857/v1/198ab2568fda911dfa51cf36.png"},{"id":68205936,"identity":"c9110ecb-66d2-4617-979e-bd9232add963","added_by":"auto","created_at":"2024-11-04 16:16:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":228088,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the patient recruitment process\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5297857/v1/df7840fe06aa280dd104ffc7.png"},{"id":68204557,"identity":"13ee4391-649d-4b9d-b897-9495a254e206","added_by":"auto","created_at":"2024-11-04 16:08:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145934,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of radiomic feature selection. \u003cstrong\u003e(A)\u003c/strong\u003e Adjustment parameter selection of the LASSO model. \u003cstrong\u003e(B) \u003c/strong\u003eThe LASSO regression feature dimension reduction. \u003cstrong\u003e(C)\u003c/strong\u003e The subset of 5 radiomics features that best predicted efficacy.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5297857/v1/adbd2b12137816239ac7a4eb.png"},{"id":68204560,"identity":"281aa2d4-b4d0-4a4e-bd78-d7734865cea2","added_by":"auto","created_at":"2024-11-04 16:08:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142979,"visible":true,"origin":"","legend":"\u003cp\u003eAUC Comparison of three models in the training \u003cstrong\u003e(A)\u003c/strong\u003e and testing \u003cstrong\u003e(B)\u003c/strong\u003e cohorts.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5297857/v1/3ab13128968e30829b016fa8.png"},{"id":68203698,"identity":"5e1842f7-e1cd-43bb-80f2-264f761178ba","added_by":"auto","created_at":"2024-11-04 16:00:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55689,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive nomogram for predicting ALNM in breast cancer.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5297857/v1/79652cab231df19490c96d15.png"},{"id":68203701,"identity":"3c76f058-981c-4d2a-bca9-701a6b05914a","added_by":"auto","created_at":"2024-11-04 16:00:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":120100,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration curves indicate that both the training(A) and testing(B) cohort combined models demonstrate higher calibration accuracy.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5297857/v1/30cc1f4f8e28ba60fee442f8.png"},{"id":68203705,"identity":"41a5ebce-e183-4064-a9b5-5623d016a59c","added_by":"auto","created_at":"2024-11-04 16:00:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":141263,"visible":true,"origin":"","legend":"\u003cp\u003eThe decision curves demonstrate that the predictive performance of the combined model surpasses that of both the clinical and radiomics models.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-5297857/v1/76a3682f79460efc01a4ea10.png"},{"id":74602477,"identity":"7e818f47-a0c3-4e13-aae4-2feca312af36","added_by":"auto","created_at":"2025-01-24 00:01:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1828821,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5297857/v1/1eccdbc6-f1b4-4842-9701-1afa15c6b7ab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDevelopment of a \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF-FDG PET/CT-based Radiomics Model for Predicting Axillary Lymph Node Metastasis in Breast Cancer\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer is one of the leading causes of cancer-related mortality and the most common cancer among female patients. According to global cancer statistics, its incidence among female patients is higher than the incidence of lung cancer[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Breast cancer\u0026rsquo;s most common metastatic pathway is ALNM. For example, the 5-year survival rate is 98.8%, for patients with localized lesions confined to the breast, while it decreases to 85.8% for those with ALNM [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The treatment plan depends on the presence or absence of ALNM. Currently, ALNM is determined by sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND). Both procedures are invasive and may result in various adverse effects, like infection, sensory abnormalities, upper limb mobility issues, and lymphedema, while also possessing a certain false-negative rate. However, compared to ALND, SLNB is less invasive and has a lower incidence of adverse effects, and it has gradually replaced ALND in clinical practice[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The treatment guidelines from the American Medical Association also recommend that no further ALND is necessary when the SLNB result is negative[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although SLNB has become the primary approach for clinically assessing ALNM, studies have shown that the negative rate of SLNB can reach 70%, with a complication rate of 41% post-SLNB[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, non-invasive evaluation of ALNM before treatment holds significant clinical importance.\u003c/p\u003e \u003cp\u003eThe preoperative assessment of ALNM in clinical practice primarily relies on imaging examinations, including breast ultrasound, mammography, etc[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the sensitivity of these examinations for ALNM remains relatively low. In contrast to other imaging modalities, PET/CT combines the high-resolution anatomical localization of CT with the high sensitivity and specificity of metabolic imaging from PET, making it widely used for non-invasive diagnosis at the molecular imaging level[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, nuclear medicine physicians commonly use maximum standardized uptake value (SUVmax) as a critical criterion for assessing the malignancy of lesions, with higher SUVmax values generally indicating a greater likelihood of malignancy. This assessment is often supplemented by other imaging characteristics of the lesions, such as morphology, volume, and density[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Numerous studies have shown a correlation between SUVmax and tumor malignancy[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the accuracy of these assessments can be significantly affected by image quality and subjective factors. In cases where lesions are small (\u0026lt;\u0026thinsp;0.5 cm), exhibit high differentiation, or grow slowly, the likelihood of false negatives increases.\u003c/p\u003e \u003cp\u003eRadiomics is a rapidly evolving research field that extracts a vast array of radiomics features from high-throughput imaging data. It offers richer imaging information, thereby reducing the limitations of subjective interpretation by physicians and enhancing the quality of medical imaging diagnosis. Research based on PET/CT radiomics is still scarce, despite the fact that several studies have used ultrasonography, CT, and MRI radiomics to predict ALNM in breast cancer[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The purpose of this study is to investigate the predictive usefulness of preoperative \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT radiomics for breast cancer ALNM, offering more data to improve clinical diagnosis and guide treatment decisions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003ePatient\u003c/p\u003e \u003cp\u003e Since this is a retrospective analysis, Jinhua Central Hospital's Ethics Committee, which is connected to Zhejiang University, has waived the need for patient-informed consent. A retrospective collection of pre-treatment \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging data, along with clinical and pathological data, was conducted for breast cancer patients confirmed by pathology at Jinhua Hospital affiliated with Zhejiang University, covering the period from August 5, 2015, to July 25, 2024. Data were rigorously screened according to inclusion and exclusion criteria. The patient selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e .\u003c/p\u003e \u003cp\u003eInclusion criteria: 1) No treatment prior to PET/CT examination, including surgery, radiotherapy, chemotherapy, and immunotherapy; 2) Both the primary breast cancer lesion and ALNM lesions were confirmed by pathology; 3) Complete clinical and pathological information.\u003c/p\u003e \u003cp\u003eExclusion criteria: 1) Individuals who were on anticancer medication prior to PET/CT scanning; 2) Inadequate clinical and pathological information; 3) Existence of more cancerous tumors; 4) Poor image quality of PET/CT, affecting interpretation of results.\u003c/p\u003e \u003cp\u003eA training cohort and a testing cohort were randomly selected from the dataset in a 6:4 ratio. Instances from the testing cohort were used for an impartial assessment of the prediction model's performance, while all instances from the training cohort were used to train the model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePET/CT Scanning Technology\u003c/p\u003e \u003cp\u003eAll enrolled patients underwent scanning using the Siemens PET/CT-Biograph mCT. Prior to the examination, patients were told to fast for 6\u0026ndash;8 hours, and their height, weight, and blood glucose levels were measured. An intravenous injection of the \u003csup\u003e18\u003c/sup\u003eF-FDG imaging agent was administered at a dose of 0.1\u0026ndash;0.15 mCi/kg, ensuring that the patient's fasting blood glucose level was below 11.1 mmol/L prior to injection. The patient was then instructed to rest quietly for 1 hour before undergoing PET/CT scanning.\u003c/p\u003e \u003cp\u003eThe following were the CT scan parameters: 120 kV tube voltage and a tube current automatic mAs technology, the slice thickness is 3 mm, pitch is 0.8, and rotation duration is 0.5 s/r. The PET scan parameters were as follows: 1.5 min/bed, with 6 to 8 bed positions, and the slice thickness of 3 mm. For post-processing, the scanned photos are uploaded to the imaging workstation.\u003c/p\u003e \u003cp\u003eImage Assessment\u003c/p\u003e \u003cp\u003eThe images are evaluated by two radiologists at our center, each with over three years of diagnostic experience, who are kept unaware of the patients' clinical information and pathology results. The evaluation criteria are as follows: A higher uptake of the radiopharmaceutical in breast tissue compared to surrounding areas indicates the presence of breast cancer, while an increased uptake in lymph nodes compared to adjacent muscle tissue suggests lymph node metastasis. A semi-automated delineation of the region of interest (ROI) is performed based on a 40% SUVmax threshold, measuring PET metabolic parameters, including SUVmax, minimum standardized uptake value (SUVmin), average standardized uptake value (SUVavg), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Additionally, the maximum diameter of the lesion in the axial plane is measured. In cases with multiple lesions, only the largest lesion by volume is selected for measurement.\u003c/p\u003e \u003cp\u003eRadiomics\u003c/p\u003e \u003cp\u003eData Collection: Export the raw DICOM data from the \"Medex\" platform. Image Segmentation: Using the 3D Slicer software (version 5.6.1), select the \"Draw\" tool to manually delineate the ROI layer by layer on the PET/CT fused images, taking care to avoid necrotic areas. Perform radiomics feature extraction on the segmented ROI using the \"Radiomics\" package for both PET and CT images. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the radiomics analysis workflow used in this investigation. Three groups of radiomics features can be distinguished: (I) geometry, (II) intensity, and (III) texture. Tumors are characterized by geometric features in three dimensions. The first-order statistical distribution of voxel intensities inside the tumor is described by intensity characteristics. The higher-order spatial distribution of the intensity patterns is described by the texture features. Z-score normalization is used to address the issue of scale variation in manually extracted radiomics features.\u003c/p\u003e \u003cp\u003eUsing a double-blind technique, two nuclear medicine doctors separately segmented each lesion. The intraclass correlation coefficient (ICC) was used to assess the stability of feature extraction; an ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.75 indicates strong consistency in feature extraction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClinical and Pathological Characteristics\u003c/p\u003e \u003cp\u003eClinical information were recorded for all included patients, including age, body mass index (BMI), menstrual status, tumor T stage, carcinoembryonic antigen (CEA) levels, carbohydrate antigen 125 (CA125) levels, and carbohydrate antigen 153 (CA153) levels. Additionally, the following pathological and immunohistochemical data were recorded: estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER-2) status, tumor cell proliferation marker 67 (Ki-67) levels, and molecular subtypes of breast cancer. ER positivity is defined as \u0026ge;\u0026thinsp;10% of tumor cell nuclei staining positive; PR positivity is defined as \u0026ge;\u0026thinsp;10% of tumor cell nuclei staining positive, with \u0026ge;\u0026thinsp;20% indicating high expression, and lower percentages indicating low expression; HER-2 positivity is defined as an immunohistochemical score of \u0026ge;\u0026thinsp;2\u0026thinsp;+\u0026thinsp;or positive FISH amplification; Ki-67 expression index\u0026thinsp;\u0026ge;\u0026thinsp;14% indicates high expression, while lower percentages indicate low expression. According to the 2023 NCCN guidelines[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], breast cancer molecular subtyping is determined based on immunohistochemical results, specifically: luminal A, luminal B, HER-2 positive, and triple-negative.\u003c/p\u003e \u003cp\u003eFeature Selection and Model Construction\u003c/p\u003e \u003cp\u003eA Mann-Whitney U test was run on all radiomics characteristics, preserving only those with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The correlation between characteristics with high redundancy was determined using Pearson's rank correlation coefficient, which kept features with a correlation coefficient of at least 0.9 between any two features. For additional feature selection, the Least Absolute Shrinkage and Selection Operator (LASSO) was utilized. To find the ideal λ, 10-fold cross-validation was utilized, keeping features with non-zero coefficients. Finally, the Rad score was calculated by summing the products of the LASSO-Logistic regression coefficients and their corresponding values. This study also compared the stability and reliability of seven machine learning algorithms in predicting ALNM, including Logistic Regression (LR), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest (RF), Extra Trees, and Extreme Gradient Boosting (XGBoost). The diagnostic performance of the optimal predictive model was evaluated using the Receiver Operating Characteristic (ROC) curve.\u003c/p\u003e \u003cp\u003eBuilding the clinical model follows a nearly identical procedure to that of building the radiomics model. First, statistical variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected from the clinical baseline data, and a clinical model was constructed using the optimal machine learning algorithm, incorporating 5-fold cross-validation and a fixed testing cohort. Finally, a nomogram was constructed based on logistic regression analysis by integrating radiomics features and clinical characteristics, and the ROC curve and calibration curve were plotted to evaluate its performance. The clinical utility was evaluated using DCA.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe Python statsmodels module (version 0.13.2) was utilized for statistical analysis, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. For continuous variables, the Mann-Whitney U test was utilized to examine intergroup differences; for categorical variables, the χ2 test was employed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical Feature\u003c/h2\u003e\n \u003cp\u003eThis study included a total of 78 pathologically confirmed female breast cancer patients, with an average age of 53.60\u0026thinsp;\u0026plusmn;\u0026thinsp;12.49 year. Among them, 57 patients had positive ALNM, while 21 had negative results. The subjects were randomly allocated into a training cohort (46 instances) and a testing group (32 cases) in a 6:3 ratio. The training cohort included 34 cases with positive ALNM and 12 cases with negative results, while the testing cohort comprised 23 positive cases and 9 negative cases. The participants in our cohort\u0026apos;s baseline clinical characteristics are displayed in\u0026nbsp;\u003cstrong\u003eTable. 1\u003c/strong\u003e. Age, BMI, tumor markers, MTV, TLG, menstrual status, and immunohistochemical indicators showed no significant differences between two cohorts (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while tumor diameter, SUVmax, SUVmin, SUVavg, T staging, and breast cancer molecular subtypes exhibited significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of patients in our cohorts.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFeatures\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eALL(n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Metastasis(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetastasis(n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.60\u0026thinsp;\u0026plusmn;\u0026thinsp;12.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.52\u0026thinsp;\u0026plusmn;\u0026thinsp;16.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.53\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor diameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.09\u0026thinsp;\u0026plusmn;\u0026thinsp;19.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.01\u0026thinsp;\u0026plusmn;\u0026thinsp;16.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.80\u0026thinsp;\u0026plusmn;\u0026thinsp;18.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenstrual status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(51.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(47.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(52.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(48.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(52.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(47.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(23.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(7.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(52.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(61.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(14.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(19.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(10.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(12.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.08\u0026thinsp;\u0026plusmn;\u0026thinsp;100.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.93\u0026thinsp;\u0026plusmn;\u0026thinsp;44.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.76\u0026thinsp;\u0026plusmn;\u0026thinsp;114.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCA125\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.73\u0026thinsp;\u0026plusmn;\u0026thinsp;301.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.99\u0026thinsp;\u0026plusmn;\u0026thinsp;362.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.54\u0026thinsp;\u0026plusmn;\u0026thinsp;278.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCA153\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.70\u0026thinsp;\u0026plusmn;\u0026thinsp;53.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.02\u0026thinsp;\u0026plusmn;\u0026thinsp;22.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.00\u0026thinsp;\u0026plusmn;\u0026thinsp;60.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(30.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(31.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54(69.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(71.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(68.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(57.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(52.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(59.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(42.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(47.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(40.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(30.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(29.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54(69.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(70.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eKi67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(21.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(23.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(21.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(78.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(76.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(78.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMol-subtypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(21.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(19.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(22.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(44.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(52.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(42.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(24.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(28.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(6.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(3.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUVmax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.41\u0026thinsp;\u0026plusmn;\u0026thinsp;6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUVmin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUVavg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMTV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.46\u0026thinsp;\u0026plusmn;\u0026thinsp;37.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.32\u0026thinsp;\u0026plusmn;\u0026thinsp;43.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.58\u0026thinsp;\u0026plusmn;\u0026thinsp;443.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.20\u0026thinsp;\u0026plusmn;\u0026thinsp;44.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190.46\u0026thinsp;\u0026plusmn;\u0026thinsp;512.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eRadiomics Features Selection and Model Establishment\u003c/h3\u003e\n\u003cp\u003eUsing the 3D Slicer program, 1,702 radiomics characteristics in total were retrieved. There were 246 features left after all features were first screened using the Mann-Whitney U test. Following that, a Pearson correlation analysis was carried out, yielding 65 residual characteristics. Finally, LASSO regression with an optimal \u0026lambda; of 0.1048 identified three non-zero coefficients for the radiomics features, establishing the Rad score(Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe following formula is used to determine the Rad score:\u003c/p\u003e\n\u003cp\u003e0.739130434782608\u0026thinsp;+\u0026thinsp;0.02911 * wavelet_LHL_glszm_SizeZoneNonUniformityNormalized_CT\u0026thinsp;+\u0026thinsp;0.047979 * wavelet_LLL_glcm_SumAverage_CT \u0026minus;\u0026thinsp;0.107920 * wavelet_HHL_glcm_JointAverage_PET\u003c/p\u003e\n\u003cp\u003eThis study compared predictive models established using seven different machine learning algorithms. The results indicated that the radiological model constructed using the LR method exhibited the best stability and reliability (\u003cstrong\u003eTable. 2\u003c/strong\u003e). Ultimately, the radiomics model based on LR was selected to develop the subsequent nomogram.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePerformance of different machine learning models in predicting ALNM in both cohorts.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpe\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR-train\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8347\u0026ndash;1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6497\u0026ndash;0.9542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u0026iuml;ve Bayes-train\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7840\u0026ndash;1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u0026iuml;ve Bayes-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6298\u0026ndash;0.9451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKNN-train\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8611\u0026ndash;0.9992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKNN-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5164\u0026ndash;0.9232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecision Tree-train\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9650\u0026ndash;1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecision Tree-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4892\u0026ndash;0.8779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Forest-train\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9660\u0026ndash;1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom Forest-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6539\u0026ndash;0.9548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtra Trees-train\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9489\u0026ndash;1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtra Trees-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5824\u0026ndash;0.9200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXG Boost-train\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0000\u0026ndash;1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXG Boost-test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4072\u0026ndash;0.8826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical Features Selection and Model Establishment\u003c/h2\u003e\n \u003cp\u003eA predictive model was developed using the clinical features of patients in the training cohort, with the univariate and multivariate analysis results for 46 breast cancer patients presented in\u0026nbsp;\u003cstrong\u003eTable. 3.\u003c/strong\u003e The results of the univariate logistic regression analysis indicated that tumor diameter, SUVmax, SUVmin, SUVavg, and tumor T stage are risk factors for ALNM. The creation of the clinical model took into account the findings of further multivariate logistic regression analysis, which showed that SUVmin (OR\u0026thinsp;=\u0026thinsp;1.175, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and tumor T stage (OR\u0026thinsp;=\u0026thinsp;1.246, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are independent risk factors for ALNM.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic regression analysis of clinical data in the training cohort.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFeature\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.010(1.004\u0026ndash;1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.004(0.989\u0026ndash;1.018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.317(1.188\u0026ndash;1.461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.246(1.051\u0026ndash;1.477)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUVmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.022(1.005\u0026ndash;1.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.026(0.355\u0026ndash;25.842)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUVavg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.034(1.006\u0026ndash;1.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.569(0.14\u0026ndash;2.316)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSUVmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.083(1.043\u0026ndash;1.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.175(1.06\u0026ndash;1.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003ePredictive Performance of Different Models\u003c/h3\u003e\n\u003cp\u003eThree models were used to assess the prediction accuracy for ALNM: the clinical model, the radiomics model, and the combination model. The training cohort\u0026apos;s AUCs were 0.917, 0.922, and 0.931, while the testing cohorts were 0.778, 0.802, and 0.821 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In both cohorts, the combined model\u0026apos;s AUC was found to be significantly higher by the DeLong test than by the clinical and radiomics models (\u003cstrong\u003eTable. 4\u003c/strong\u003e). Ultimately, a nomogram was created by merging the extremely stable and dependable Rad score derived from logistic regression with clinical prognostic criteria (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The calibration curve showed that the combined model fit well to forecast the spread of breast cancer to the axillary lymph nodes (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Furthermore, the DCA showed that there is a substantial net advantage offered by the combined approach (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDelong test.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCombined Vs Clinic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCombined Vs Rad\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRad Vs Clinic\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs one of the most prevalent cancers in females, breast cancer affects a younger and younger population globally each year in terms of incidence [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Precise determination of lymph node staging in breast cancer is essential for choosing therapeutic treatment strategies and forms a significant foundation for prognostic assessment. The primary method for assessing ALNM in breast cancer is SLNB; however, this procedure may result in postoperative complications and impact recovery. A non-invasive method for forecasting the metastases of axillary lymph nodes is provided by radiomics. Radiomics provides a non-invasive means of predicting metastases from axillary lymph nodes by supplying features from images that are not visible through conventional imaging assessment methods. These features are often linked to genomic, cellular, and metabolic information relevant to tumor biology[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Some studies have utilized MRI radiomics to predict pelvic lymph node metastasis in cervical cancer, reporting AUC values of 0.82, 0.84, and 0.9 [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A meta-analysis using ultrasound radiomics has also been used in papers summarizing prognostic research on cervical lymph node metastasis in thyroid cancer, with a sensitivity value of 0.81 (95% CI, 0.73–0.86) and a specificity value of 0.87 (95% CI, 0.83–0.91) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Zhao et al.'s[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] study, which had an AUC of 0.91, showed how CT radiomics analysis might improve preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma. The aforementioned studies indicate that conventional radiomics demonstrates a strong predictive capability for lymph node metastasis in tumors.\u003c/p\u003e\u003cp\u003eThis work developed a hybrid model that uses \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT radiomics and clinical variables to predict the metastasis of axillary lymph nodes in breast cancer. Independent validation with a testing cohort confirmed the model's strong predictive capability. This study included five metabolic parameters from PET/CT in the clinical characteristics. Statistical analysis indicated that SUVmin demonstrated a strong predictive capability within our cohort. In related studies on PET/CT radiomics, various metabolic parameters have frequently shown significant predictive abilities. For instance, research by Moazemi et al.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] revealed that the SUVmin from pre-treatment \u003csup\u003e68\u003c/sup\u003eGa-PSMA PET/CT may serve as a potential predictor of overall survival in patients with advanced prostate cancer undergoing \u003csup\u003e177\u003c/sup\u003eLu-PSMA therapy. This may be attributed to the fact that low SUV values can indicate a certain level of tumor heterogeneity, which may be associated with a broader spectrum of activity values within the tumor.\u003c/p\u003e\u003cp\u003eThis study extracted a total of 1,702 radiomics features through data analysis. After conducting differential analysis and LASSO regression, three radiomics features were selected for model construction. These features are closely related to the regional texture, gray values, contrast, and frequency domain characteristics of breast cancer lesions. They exhibit partial similarity to the radiomics features used in the comprehensive predictive model developed by Li et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], suggesting that PET/CT imaging manifestations of different primary breast cancer lesions present various subtle differences.\u003c/p\u003e\u003cp\u003eThe combined model in this investigation did well in terms of predictive power. In the training cohort, the AUC for predicting the metastasis of axillary lymph nodes was 0.931, whereas in the testing cohort, it was 0.821. Prior research has demonstrated that conventional clinical characteristics can forecast the ALNM of breast cancer. However, compared to models relying on SUVmin and tumor T staging clinical indicators, the radiomic model demonstrated higher accuracy in both cohorts. This suggests that radiomics may possess a superior ability to identify tumor heterogeneity in breast cancer. We created a combined predictive model to assess whether clinical factors could improve the radiomics model's performance even further. According to the results, the combined model's prediction capacity outperformed the radiomics model alone in both cohorts, according to the DeLong test. This indicates that the combined model can integrate both types of information, capturing a more comprehensive set of features and thereby improving predictive accuracy.\u003c/p\u003e\u003cp\u003eOne of the study's limitations is that it is a retrospective study conducted at a single location, which raises the possibility of selection bias. Furthermore, because of the small sample size, it's possible that several indicators that revealed differences in univariate analysis won't reach statistical significance in the cohort's multivariate analysis. Finally, this study did not exclude patients with multifocal breast cancer. Future multicenter, large-sample, prospective studies are expected to improve the treatment of breast cancer by further validating the prognostic usefulness of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging for ALNM.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe multivariable model developed from \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT radiomics and clinical features demonstrates strong predictive performance for ALNM in breast cancer. This model is expected to serve as a reference for personalized precision treatment decisions in clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eALNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eAxillary lymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003ePET/CT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003ePositron emission tomography /computed tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eArea under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eDecision curve analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eSLNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eSentinel lymph node biopsy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eALND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eAxillary lymph node dissection\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eROI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eRegion of interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eSUVmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eMaximum standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eSUVmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eMinimum standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eSUVavg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eAverage standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eMTV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eMetabolic tumor volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eTLG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eTotal lesion glycolysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eIntraclass correlation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eCarcinoembryonic antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eCA125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eCarbohydrate antigen 125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eCA153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eCarbohydrate antigen 153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eEstrogen receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eProgesterone receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eHER-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eHuman epidermal growth factor receptor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eKi-67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eTumor cell proliferation marker 67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eLeast absolute shrinkage and selection operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eLogistic Regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eKNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eK-Nearest Neighbors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eXGBoost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eExtreme Gradient Boosting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3707%;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84.6293%;\"\u003e\n \u003cp\u003eReceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003e This retrospective study was approved by the Biomedical Ethics Committee of Jin Hua Municipal Central Hospital, which waived the requirement for patient informed consent.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by Zhejiang Province Public Welfare Technology Application Research Project (LGF21H180007).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZY: Conceptualization, Data curation \u0026amp; analysis, Writing \u0026ndash; original draft. KD: Funding acquisition, Resources, Software, Writing \u0026ndash; original draft. QH: Project administration, Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing. All authors participated in the revision and improvement of the manuscript to ensure the accuracy and completeness of the content.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all the participants and all the researchers and collaborators who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung, H, Ferlay, J, Siegel, RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA-CANCER J CLIN. 2021; 71 CA-CANCER J CLIN. \u003c/li\u003e\n\u003cli\u003eChang JM, Leung JWT, Moy L, Ha SM, Moon WK. Axillary Nodal Evaluation in Breast Cancer: State of the Art. Radiology. 2020 Jun;295(3):500-515. \u003c/li\u003e\n\u003cli\u003eGiuliano AE, Ballman KV, McCall L, Beitsch PD, Brennan MB, Kelemen PR, Ollila DW, Hansen NM, Whitworth PW, Blumencranz PW, Leitch AM, Saha S, Hunt KK, Morrow M. Effect of Axillary Dissection vs No Axillary Dissection on 10-Year Overall Survival Among Women With Invasive Breast Cancer and Sentinel Node Metastasis: The ACOSOG Z0011 (Alliance) Randomized Clinical Trial. JAMA. 2017 Sep 12;318(10):918-926. \u003c/li\u003e\n\u003cli\u003eLyman GH, Somerfield MR, Bosserman LD, Perkins CL, Weaver DL, Giuliano AE. Sentinel Lymph Node Biopsy for Patients With Early-Stage Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline Update. J Clin Oncol. 2017 Feb 10;35(5):561-564. doi: 10.1200/JCO.2016.71.0947. Epub 2016 Dec 12.\u003c/li\u003e\n\u003cli\u003eZhang X, Liu M, Ren W, Sun J, Wang K, Xi X, Zhang G. Predicting of axillary lymph node metastasis in invasive breast cancer using multiparametric MRI dataset based on CNN model. Front Oncol. 2022 Dec 6;12:1069733. \u003c/li\u003e\n\u003cli\u003eC\u0026ouml;mert, D, van Gils, CH, Veldhuis, WB, et al. Challenges and Changes of the Breast Cancer Screening Paradigm. J MAGN RESON IMAGING. 2023; 57 J MAGN RESON IMAGING. \u003c/li\u003e\n\u003cli\u003eFarwell, MD, Pryma, DA, Mankoff, DA. PET/CT imaging in cancer: current applications and future directions. CANCER-AM CANCER SOC. 2014; 120 (22): 3433-45. \u003c/li\u003e\n\u003cli\u003eNiu, R, Gao, J, Shao, X, et al. Maximum Standardized Uptake Value of 18F-deoxyglucose PET Imaging Increases the Effectiveness of CT Radiomics in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules. Front Oncol. 2021; 11 Front Oncol.\u003c/li\u003e\n\u003cli\u003ede Koster EJ, Noortman WA, Mostert JM, Booij J, Brouwer CB, de Keizer B, de Klerk JMH, Oyen WJG, van Velden FHP, de Geus-Oei LF, Vriens D; EfFECTS trial study group. Quantitative classification and radiomics of [\u003csup\u003e18\u003c/sup\u003eF]FDG-PET/CT in indeterminate thyroid nodules. Eur J Nucl Med Mol Imaging. 2022 Jun;49(7):2174-2188. doi: 10.1007/s00259-022-05712-0. Epub 2022 Feb 9. \u003c/li\u003e\n\u003cli\u003eLi Y, Han D, Shen C. Prediction of the axillary lymph-node metastatic burden of breast cancer by \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT-based radiomics. BMC Cancer. 2024 Jun 7;24(1):704. \u003c/li\u003e\n\u003cli\u003eLi, X, Yang, L, Jiao, X. Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer. ACAD RADIOL. 2023; 30 ACAD RADIOL.\u003c/li\u003e\n\u003cli\u003eGradishar, WJ, Moran, MS, Abraham, J, et al. Breast Cancer, Version 3.2024, NCCN Clinical Practice Guidelines in Oncology. J NATL COMPR CANC NE. 2024; 22 (5): 331-357.\u003c/li\u003e\n\u003cli\u003eGiaquinto, AN, Sung, H, Newman, LA, et al. Breast cancer statistics 2024. CA-CANCER J CLIN. 2024; CA-CANCER J CLIN.\u003c/li\u003e\n\u003cli\u003eLee, G, Lee, H, Ko, E, et al. Radiomics and imaging genomics in precision medicine PRECIS FUTURE MED. 2024; 1 PRECIS FUTURE MED\u003c/li\u003e\n\u003cli\u003eLee, G, Lee, HY, Park, H, et al. Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. EUR J RADIOL. 2016; 86 297-307.\u003c/li\u003e\n\u003cli\u003eSong, J, Hu, Q, Ma, Z, et al. Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients. EUR RADIOL. 2021; 31 (9): 6938-6948.\u003c/li\u003e\n\u003cli\u003eWang, T, Li, YY, Ma, NN, et al. A MRI radiomics-based model for prediction of pelvic lymph node metastasis in cervical cancer. World J Surg Oncol. 2024; 22 (1): 55.\u003c/li\u003e\n\u003cli\u003eXia, X, Li, D, Du, W, et al. Radiomics Based on Nomogram Predict Pelvic Lymph node Metastasis in Early-Stage Cervical Cancer. Diagnostics (Basel). 2022; 12 (10):\u003c/li\u003e\n\u003cli\u003eMu, J, Cao, Y, Zhong, X, et al. Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models. BRIT J RADIOL. 2024; 97 BRIT J RADIOL.\u003c/li\u003e\n\u003cli\u003eZhao, X, Li, W, Zhang, J, et al. Radiomics analysis of CT imaging improves preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma. EUR RADIOL. 2023; 33 EUR RADIOL.\u003c/li\u003e\n\u003cli\u003eMoazemi, S, Erle, A, L\u0026uuml;tje, S, et al. Estimating the Potential of Radiomics Features and Radiomics Signature from Pretherapeutic PSMA-PET-CT Scans and Clinical Data for Prediction of Overall Survival When Treated with 177Lu-PSMA. Diagnostics (Basel). 2021; 11 (2): \u003c/li\u003e\n\u003cli\u003eLi, Y, Han, D, Shen, C, et al. Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study. BMC Cancer. 2023; 23 BMC Cancer.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, PET/CT, Radiomics, Axillary lymph node metastasis","lastPublishedDoi":"10.21203/rs.3.rs-5297857/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5297857/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAxillary lymph node metastasis (ALNM) status is an important factor for the determination of the therapeutic strategies and breast cancer prognosis. In our study, we investigate whether radiomics features from \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose(\u003csup\u003e18\u003c/sup\u003eF-FDG) positron emission tomography /computed tomography (PET/CT), combined with clinical or pathological characteristics, provide a higher predictive value of ALNM.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was performed on 78 female patients who underwent preoperative \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT scans at Jinhua Central Hospital from August 2015 to July 2024, with a mean age of 53.60\u0026thinsp;\u0026plusmn;\u0026thinsp;12.49 years (range: 35\u0026ndash;84 years). The cases were randomly divided into a training cohort (46 cases) and a testing cohort (32 cases) in a 6:4 ratio. All patients' PET/CT and clinical pathological features were analyzed, and radiomics features were extracted from the PET/CT images. Subsequently, we developed radiomics, clinical, and combined radiomics-clinical models. We also assessed the performance of these three models in predicting ALNM. The Python stats models package (version 0.13.2) was used for statistical analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFor the three features radiomics model and combined model in the training cohort, the area under the curve (AUC) was 0.922 and 0.931, which were both higher than that of the traditional clinical feature model (AUC\u0026thinsp;=\u0026thinsp;0.917). The AUC values for the three models in the testing cohort were 0.802, 0.821, and 0.778. For predicting ALNM across all cohorts, the radiomics model and the combined model showed clinical benefit in the decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe PET/CT-based radiomics model demonstrated strong efficacy in predicting ALNM for breast cancer and has clinical application value.\u003c/p\u003e","manuscriptTitle":"Development of a 18F-FDG PET/CT-based Radiomics Model for Predicting Axillary Lymph Node Metastasis in Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-04 16:00:23","doi":"10.21203/rs.3.rs-5297857/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d4f97713-059a-44c3-ae26-8749ce05ff36","owner":[],"postedDate":"November 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-23T23:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-04 16:00:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5297857","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5297857","identity":"rs-5297857","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}