Diagnostic Values of 8 Different Imaging Modalities for Preoperative Detection of Axillary Lymph Node Metastasis of Breast Cancer: A Bayesian Network Meta-analysis

Comparative Study of Different Imaging Modalities for Diagnosis of Bone Metastases of Prostate Cancer: A Bayesian Network Meta-analysis

PURPOSE

This study aimed to compare the diagnostic performances of 8 different imaging modalities for preoperative detection of bone metastases in prostate cancer patients by performing a network meta-analysis using direct comparison studies with 2 or more imaging techniques.

PATIENTS AND METHODS

We searched PubMed, Embase, and Cochrane Library for studies evaluating the performances of 8 different imaging modalities for the preoperative detection of bone metastases in prostate cancer patients. The network meta-analysis was performed in patient-based analysis. The consistency was evaluated by examining the agreement between direct and indirect treatment effects, and the surface under the cumulative ranking curve (SUCRA) values were obtained to calculate the probability of each imaging modality being the most effective diagnostic method.

RESULTS

A total of 999 patients from 13 direct comparison studies using 8 different imaging modalities for preoperative detection or follow-up of bone metastases in prostate cancer patients were included. For the detection of bone metastases of prostate cancer, 68 Ga-PSMA-11 PET/CT showed the highest SUCRA values of sensitivity, positive predictive value, accuracy, and diagnostic odds ratio. In addition, 18 F-NaF PET/CT and SPECT/CT showed high SUCRA values.

CONCLUSIONS

68 Ga-PSMA-11 PET/CT showed the highest SUCRA values. Other imaging modalities showed complementary diagnostic roles for preoperative detection of bone metastases in patients with prostate cancer, except bone scintigraphy and MRI.

Invasive Lobular Carcinoma: A Review of Imaging Modalities with Special Focus on Pathology Concordance

Invasive lobular cancer (ILC) is the second most common type of breast cancer. It is characterized by a unique growth pattern making it difficult to detect on conventional breast imaging. ILC can be multicentric, multifocal, and bilateral, with a high likelihood of incomplete excision after breast-conserving surgery. We reviewed the conventional as well as newly emerging imaging modalities for detecting and determining the extent of ILC- and compared the main advantages of MRI vs. contrast-enhanced mammogram (CEM). Our review of the literature finds that MRI and CEM clearly surpass conventional breast imaging in terms of sensitivity, specificity, ipsilateral and contralateral cancer detection, concordance, and estimation of tumor size for ILC. Both MRI and CEM have each been shown to enhance surgical outcomes in patients with newly diagnosed ILC that had one of these imaging modalities added to their preoperative workup.

Different Imaging Modalities for the Diagnosis of Axillary Lymph Node Metastases in Breast Cancer: A Systematic Review and Network Meta-Analysis of Diagnostic Test Accuracy

BACKGROUND

Accurate diagnosis of axillary lymph node metastasis (ALNM) of breast cancer patients is important to guide local and systemic treatment.

PURPOSE

To evaluate the diagnostic performance of different imaging modalities for ALNM in patients with breast cancer.

STUDY TYPE

Systematic review and network meta-analysis (NMA).

SUBJECTS

Sixty-one original articles with 8011 participants.

FIELD STRENGTH

1.5 T and 3.0 T.

ASSESSMENT

We used the QUADAS-2 and QUADAS-C tools to assess the risk of bias in eligible studies. The identified articles assessed ultrasonography (US), MRI, mammography, ultrasound elastography (UE), PET, CT, PET/CT, scintimammography, and PET/MRI.

STATISTICAL ANALYSIS

We used random-effects conventional meta-analyses and Bayesian network meta-analyses for data analyses. We used sensitivity and specificity, relative sensitivity and specificity, superiority index, and summary receiver operating characteristic curve (SROC) analysis to compare the diagnostic value of different imaging modalities.

RESULTS

Sixty-one studies evaluated nine imaging modalities. At patient level, sensitivities of the nine imaging modalities ranged from 0.27 to 0.84 and specificities ranged from 0.84 to 0.95. Patient-based NMA showed that UE had the highest superiority index (5.95) with the highest relative sensitivity of 1.13 (95% confidence interval [CI]: 0.93-1.29) among all imaging methods when compared to US. At lymph node level, MRI had the highest superiority index (6.91) with highest relative sensitivity of 1.13 (95% CI: 1.01-1.23) and highest relative specificity of 1.11 (95% CI: 0.95-1.23) among all imaging methods when compared to US. SROCs also showed that UE and MRI had the largest area under the curve (AUC) at patient level and lymph node level of 0.92 and 0.94, respectively.

DATA CONCLUSION

UE and MRI may be superior to other imaging modalities in the diagnosis of ALNM in breast cancer patients at the patient level and the lymph node level, respectively. Further studies are needed to provide high-quality evidence to validate our findings.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network

The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) “breast tissue”, (2) “benign lymph nodes”, and (3) “suspicious lymph nodes”. Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a “real-world” dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the “real-world” dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93–0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms.