Multiparametric and Multimodality Functional Radiological Imaging for Breast Cancer Diagnosis and Early Treatment Response Assessment

The value of dynamic contrast-enhanced magnetic resonance imaging combined with apparent diffusion coefficient in the differentiation of benign and malignant diseases of the breast

BACKGROUND

The value of combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) histogram analysis for the diagnosis of breast cancer has not been evaluated in previous studies.

PURPOSE

To investigate the diagnostic value of DCE-MRI combined with ADC in benign and malignant breast lesions.

MATERIAL AND METHODS

The clinicopathological imaging data included 168 patients (177 lesions) with breast lesions who underwent convention breast MRI, DCE-MRI, and diffusion-weighted imaging (DWI); they were divided into the benign lesion group (n = 39) and malignant lesion group (n = 129) based on pathology.

RESULTS

Using the type III outflow curve as a diagnostic criterion for malignant breast lesions, the diagnostic sensitivity was 76.9%, the specificity was 80%, the correct rate was 72.2%, and its area under the curve (AUC) was 0.823. Using an enhancement ratio > 100% as a diagnostic criterion for malignant breast lesions, the sensitivity was 61.5%, specificity was 80%, and AUC was 0.723. Using > 3 ipsilateral vessels as a diagnostic criterion for malignant lesions in the breast resulted in a diagnostic sensitivity of 81.6%, a specificity of 80.8%, and an AUC of 0.805.

CONCLUSION

The type of time intensity curve DCE-MRI, the early enhancement rate in the first phase, the number of ipsilateral vessels, and the ADC full volume histogram of the blood supply score and DWI are valuable in the diagnosis of benign and malignant breast lesions.

A Review of the Role of the S-Detect Computer-Aided Diagnostic Ultrasound System in the Evaluation of Benign and Malignant Breast and Thyroid Masses

Computer-aided diagnosis (CAD) systems have attracted extensive attention owing to their performance in the field of image diagnosis and are rapidly becoming a promising auxiliary tool in medical imaging tasks. These systems can quantitatively evaluate complex medical imaging features and achieve efficient and high-diagnostic accuracy. Deep learning is a representation learning method. As a major branch of artificial intelligence technology, it can directly process original image data by simulating the structure of the human brain neural network, thus independently completing the task of image recognition. S-Detect is a novel and interactive CAD system based on a deep learning algorithm, which has been integrated into ultrasound equipment and can help radiologists identify benign and malignant nodules, reduce physician workload, and optimize the ultrasound clinical workflow. S-Detect is becoming one of the most commonly used CAD systems for ultrasound evaluation of breast and thyroid nodules. In this review, we describe the S-Detect workflow and outline its application in breast and thyroid nodule detection. Finally, we discuss the difficulties and challenges faced by S-Detect as a precision medical tool in clinical practice and its prospects.

Whole-body magnetic resonance imaging for prostate cancer assessment: Current status and future directions

Over the past decade, updated definitions for the different stages of prostate cancer and risk for distant disease, along with the advent of new therapies, have remarkably changed the management of patients. The two expectations from imaging are accurate staging and appropriate assessment of disease response to therapies. Modern, next-generation imaging (NGI) modalities, including whole-body magnetic resonance imaging (WB-MRI) and nuclear medicine (most often prostate-specific membrane antigen [PSMA] positron emission tomography [PET]/computed tomography [CT]) bring added value to these imaging tasks. WB-MRI has proven its superiority over bone scintigraphy (BS) and CT for the detection of distant metastasis, also providing reliable evaluations of disease response to treatment. Comparison of the effectiveness of WB-MRI and molecular nuclear imaging techniques with regard to indications and the definition of their respective/complementary roles in clinical practice is ongoing. This paper illustrates the evolution of WB-MRI imaging protocols, defines the current state-of-the art, and highlights the latest developments and future challenges. The paper presents and discusses WB-MRI indications in the care pathway of men with prostate cancer in specific key situations: response assessment of metastatic disease, "all in one" cancer staging, and oligometastatic disease.

Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p < 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10-3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine.

23 Na-MRI as a Noninvasive Biomarker for Cancer Diagnosis and Prognosis

The influx of sodium (Na⁺) ions into a resting cell is regulated by Na⁺ channels and by Na⁺/H⁺ and Na⁺/Ca²⁺ exchangers, whereas Na⁺ ion efflux is mediated by the activity of Na⁺/K⁺‐ATPase to maintain a high transmembrane Na⁺ ion gradient. Dysfunction of this system leads to changes in the intracellular sodium concentration that promotes cancer metastasis by mediating invasion and migration. In addition, the accumulation of extracellular Na⁺ ions in cancer due to inflammation contributes to tumor immunogenicity. Thus, alterations in the Na⁺ ion concentration may potentially be used as a biomarker for malignant tumor diagnosis and prognosis. However, current limitations in detection technology and a complex tumor microenvironment present significant challenges for the in vivo assessment of Na⁺ concentration in tumor. ²³Na‐magnetic resonance imaging (²³Na‐MRI) offers a unique opportunity to study the effects of Na⁺ ion concentration changes in cancer. Although challenged by a low signal‐to‐noise ratio, the development of ultrahigh magnetic field scanners and specialized sodium acquisition sequences has significantly advanced ²³Na‐MRI. ²³Na‐MRI provides biochemical information that reflects cell viability, structural integrity, and energy metabolism, and has been shown to reveal rapid treatment response at the molecular level before morphological changes occur. Here we review the basis of ²³Na‐MRI technology and discuss its potential as a direct noninvasive in vivo diagnostic and prognostic biomarker for cancer therapy, particularly in cancer immunotherapy. We propose that ²³Na‐MRI is a promising method with a wide range of applications in the tumor immuno‐microenvironment research field and in cancer immunotherapy monitoring.

Level of Evidence

2

Technical Efficacy

Stage 2

Application and Analysis of Biomedical Imaging Technology in Early Diagnosis of Breast Cancer

Breast cancer is the primary malignant tumor that endangers women's health. The incidence of breast cancer is increasing rapidly in recent years. Accurate disease evaluation before treatment is the key to the selection of treatment options. Biomedical imaging technology plays an irreplaceable role in the diagnosis and staging of tumors. Various imaging methods can provide excellent temporal and spatial resolution from multiple levels and perspectives and have become one of the most commonly used means of breast cancer early detection. With the development of radiomics, it has been found that early imaging diagnosis of breast cancer plays an important guiding role in clinical decision-making. The purpose of this study is to explore the characteristics of various breast cancer imaging technologies, promote the development of individualized accurate diagnosis and treatment of imaging, and improve the clinical application value of radiomics in the early diagnosis of breast cancer.

Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: Preliminary results

PURPOSE

Deep learning is emerging in radiology due to the increased computational capabilities available to reading rooms. These computational developments have the ability to mimic the radiologist and may allow for more accurate tissue characterization of normal and pathological lesion tissue to assist radiologists in defining different diseases. We introduce a novel tissue signature model based on tissue characteristics in breast tissue from multiparametric magnetic resonance imaging (mpMRI). The breast tissue signatures are used as inputs in a stacked sparse autoencoder (SSAE) multiparametric deep learning (MPDL) network for segmentation of breast mpMRI.

METHODS

We constructed the MPDL network from SSAE with 5 layers with 10 nodes at each layer. A total cohort of 195 breast cancer subjects were used for training and testing of the MPDL network. The cohort consisted of a training dataset of 145 subjects and an independent validation set of 50 subjects. After segmentation, we used a combined SAE-support vector machine (SAE-SVM) learning method for classification. Dice similarity (DS) metrics were calculated between the segmented MPDL and dynamic contrast enhancement (DCE) MRI-defined lesions. Sensitivity, specificity, and area under the curve (AUC) metrics were used to classify benign from malignant lesions.

RESULTS

The MPDL segmentation resulted in a high DS of 0.87 ± 0.05 for malignant lesions and 0.84 ± 0.07 for benign lesions. The MPDL had excellent sensitivity and specificity of 86% and 86% with positive predictive and negative predictive values of 92% and 73%, respectively, and an AUC of 0.90.

CONCLUSIONS

Using a new tissue signature model as inputs into the MPDL algorithm, we have successfully validated MPDL in a large cohort of subjects and achieved results similar to radiologists.

The Evolving Role of FDG-PET/CT in the Diagnosis, Staging, and Treatment of Breast Cancer

The applications of 2-deoxy-2-[¹⁸F]fluoro-D-glucose positron emission tomography/X-ray computed tomography (PET/CT) in the management of patients with breast cancer have been extensively studied. According to these studies, PET/CT is not routinely performed for the diagnosis of primary breast cancer, although PET/CT in specific subtypes of breast cancer correlates with histopathologic features of the primary tumor. PET/CT can detect metastases to mediastinal, axial, and internal mammary nodes, but it cannot replace the sentinel node biopsy. In detection of distant metastases, this imaging tool may have a better accuracy in detecting lytic bone metastases compared to bone scintigraphy. Thus, PET/CT is recommended when advanced-stage disease is suspected, and conventional modalities are inconclusive. Also, PET/CT has a high sensitivity and specificity to detect loco-regional recurrence and is recommended in asymptomatic patients with rising tumor markers. Numerous studies support the future role of PET/CT in prediction of response to neoadjuvant chemotherapy (NAC). PET/CT has a higher diagnostic value for prognostic risk stratification in comparison with conventional modalities. With the continuing research on the treatment planning and evaluation of patients with breast cancer, the role of PET/CT can be further extended.

Imaging Considerations and Interprofessional Opportunities in the Care of Breast Cancer Patients in the Neoadjuvant Setting

OBJECTIVE

To discuss standard-of-care and emerging imaging techniques employed for screening and detection, diagnosis and staging, monitoring response to therapy, and guiding cancer treatments.

DATA SOURCES

Published journal articles indexed in the National Library of Medicine database and relevant websites.

CONCLUSION

Imaging plays a fundamental role in the care of cancer patients and specifically, breast cancer patients in the neoadjuvant setting, providing an excellent opportunity for interprofessional collaboration between oncologists, researchers, radiologists, and oncology nurses. Quantitative imaging strategies to assess cellular, molecular, and vascular characteristics within the tumor is needed to better evaluate initial diagnosis and treatment response.

IMPLICATIONS FOR NURSING PRACTICE

Nurses caring for patients in all settings must continue to seek education on emerging imaging techniques. Oncology nurses provide education about the test, ensure the patient has appropriate pre-testing instructions, and manage patient expectations about timing of results availability.

Vision 20/20: Simultaneous CT-MRI--Next chapter of multimodality imaging

Multimodality imaging systems such as positron emission tomography-computed tomography (PET-CT) and MRI-PET are widely available, but a simultaneous CT-MRI instrument has not been developed. Synergies between independent modalities, e.g., CT, MRI, and PET/SPECT can be realized with image registration, but such postprocessing suffers from registration errors that can be avoided with synchronized data acquisition. The clinical potential of simultaneous CT-MRI is significant, especially in cardiovascular and oncologic applications where studies of the vulnerable plaque, response to cancer therapy, and kinetic and dynamic mechanisms of targeted agents are limited by current imaging technologies. The rationale, feasibility, and realization of simultaneous CT-MRI are described in this perspective paper. The enabling technologies include interior tomography, unique gantry designs, open magnet and RF sequences, and source and detector adaptation. Based on the experience with PET-CT, PET-MRI, and MRI-LINAC instrumentation where hardware innovation and performance optimization were instrumental to construct commercial systems, the authors provide top-level concepts for simultaneous CT-MRI to meet clinical requirements and new challenges. Simultaneous CT-MRI fills a major gap of modality coupling and represents a key step toward the so-called "omnitomography" defined as the integration of all relevant imaging modalities for systems biology and precision medicine.