Usefulness of Staging Chest CT in Breast Cancer: Evaluating Diagnostic Yield of Chest CT According to the Molecular Subtype and Clinical Stage

Prediction of Clinical Molecular Typing of Breast Invasive Ductal Carcinoma Using 18F-FDG PET/CT Dual-Phase Imaging

RATIONALE AND OBJECTIVES

To investigate the diagnostic value of Fluorine-18-labeled 2-fluoro-2-deoxy-D-glucose positron emission tomography and computed tomography (18F-FDG PET/CT) dual-phase imaging for the different molecular subtypes of invasive ductal carcinoma of the breast.

MATERIALS AND METHODS

Clinical imaging data of 164 women with invasive ductal carcinoma of the breast confirmed by pathology who underwent 18F-FDG PET/CT dual-phase imaging were retrospectively analyzed. The maximum standard uptake values (SUVmax) of the early and delayed phases of the lesion were measured and recorded as SUVmax1 and SUVmax2, respectively, and the retention index (RI) was calculated. We analyzed the change rule of SUVmax1, SUVmax2, and RI for the different molecular subtypes and molecular marker expression groups. The diagnostic threshold of different molecular marker expression status was determined using receiver operating characteristic curve analysis.

RESULTS

SUVmax1 and SUVmax2 were highest in the TNBC group and lowest in the luminal A group (p<0.001). TNBC and HER2 overexpression groups had higher RI than the luminal A and B groups (p<0.001), with no significant difference between the TNBC and HER2 overexpression groups or between the luminal A and B groups (p=0.640 and 0.345, respectively). The ER- and PR-negative groups had significantly higher SUVmax1, SUVmax2, and RI than the PR-positive group (p<0.001). The HER2-positive group had higher SUVmax1 and SUVmax2 than the negative group (p<0.001). The Ki67 overexpression group had higher SUVmax1 and SUVmax2 levels than the low expression group (p<0.001). There was no significant difference in RI between HER2-positive and negative groups or between Ki67 high and low expression groups (p=0.904 and 0.216, respectively). For ER-negative and positive expression status, the maximum area under the curve (AUC) of SUVmax2 was 0.852, diagnostic threshold was 10.87, sensitivity was 79.6%, and specificity was 74.5%. For PR-negative and positive expression status, the AUC of SUVmax2 was 0.858, diagnostic threshold was 10.45, sensitivity was 83.1%, and specificity was 75.3%. For HER2-negative and positive expression status, the AUC of SUVmax1 was 0.714, diagnostic threshold was 9.28, sensitivity was 79.6%, and specificity was 60.9%. For Ki67 high- and low expression status, the AUC of SUVmax2 was 0.915 at maximum, diagnostic threshold was 10.21, sensitivity was 83.4%, and specificity was 93.9%.

CONCLUSION

18F-FDG PET/CT dual-phase imaging facilitates the prediction of the expression of molecular markers and subtypes of invasive ductal carcinoma of the breast and the development of more tailored treatment plans for patients with this disease.

The Value of 320-Slice Spiral Computed Tomography Perfusion Imaging in Staging and Long-Term Dynamic Evaluation of Breast Cancer

The value of 320-slice spiral computed tomography (CT) perfusion imaging in staging and long-term dynamic evaluation of breast cancer was explored. 120 breast cancer patients who underwent preoperative CT examination and were confirmed by surgery and pathology were selected. All patients underwent preoperative TNM staging of breast cancer, with 120 cases in each stage. According to the results of 320-slice spiral CT, the postoperative pathology and surgical methods were compared and analyzed. CT diagnosis of breast cancer showed that T1 sensitivity was 71% and accuracy was 61%, T2 sensitivity was 74% and accuracy was 64%, T3 sensitivity was 94% and the accuracy was 84%, and the T4 sensitivity was 100% and the accuracy was 91%. The sensitivity of N1 stage was 71%, and the accuracy was 61%; and the sensitivity of N2 ~ N3 stage was 81%, and the accuracy was 76%. There were 7 cases of M1 with distant metastasis, the sensitivity was 71%, and the accuracy was 71%. At T1 stage, blood flow (BF) was 39.2 ± 16.7 mL/min/100 g, blood volume (BV) was 2.66 ± 1.4 mL/100 g, mean transit time (MTT) was 8.16 ± 2.7 s, and permeability surface (PS) was 16.6 ± 9.7 mL/min/100 g. 320-slice spiral CT perfusion imaging technology provided a new diagnostic mode for everyone, which can quantitatively identify breast cancer with multiple parameters, which was of great significance for clinical auxiliary diagnosis.

Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study

Objectives

The molecular subtype plays an important role in breast cancer, which is the main reference to guide treatment and is closely related to prognosis. The objective of this study was to explore the potential of the non-contrast-enhanced chest CT-based radiomics to predict breast cancer molecular subtypes non-invasively.

Methods

A total of 300 breast cancer patients (153 luminal types and 147 non-luminal types) who underwent routine chest CT examination were included in the study, of which 220 cases belonged to the training set and 80 cases to the time-independent test set. Identification of the molecular subtypes is based on immunohistochemical staining of postoperative tissue samples. The region of interest (ROI) of breast masses was delineated on the continuous slices of CT images. Forty-two models to predict the luminal type of breast cancer were established by the combination of six feature screening methods and seven machine learning classifiers; 5-fold cross-validation (cv) was used for internal validation. Finally, the optimal model was selected for external validation on the independent test set. In addition, we also took advantage of SHapley Additive exPlanations (SHAP) values to make explanations of the machine learning model.

Results

During internal validation, the area under the curve (AUC) values for different models ranged from 0.599 to 0.842, and the accuracy ranged from 0.540 to 0.775. Eventually, the LASSO_SVM combination was selected as the final model, which included 9 radiomics features. The AUC, accuracy, sensitivity, and specificity of the model to distinguish luminal from the non-luminal type were 0.842 [95% CI: 0.728−0.957], 0.773, 0.818, and 0.773 in the training set and 0.757 [95% CI: 0.640–0.866], 0.713, 0.767, and 0.676 in the test set.

Conclusion

The radiomics based on chest CT may provide a new idea for the identification of breast cancer molecular subtypes.