The potential of predictive and prognostic breast MRI (P2-bMRI)

Role of oedema and shrinkage patterns for prediction of response to neoadjuvant chemotherapy and survival outcomes in luminal breast cancer

AIMS

To explore the independent and additional value of oedema and shrinkage patterns for predicting the disease-free survival (DFS) and neoadjuvant chemotherapy (NAC) response in luminal breast cancer (BC).

MATERIALS AND METHODS

Patients with luminal BC who underwent NAC were enrolled in this study from 2017 to 2022. Traditional MRI features include BI-RADS-based MRI descriptors, tumor size, and ADC values, while emerging MRI features include oedema and shrinkage patterns, all of which were evaluated before, early, and after NAC. The changes in features during NAC were also evaluated. The value of features was evaluated through univariate, multivariate analyses.

RESULTS

A total of 258 patients were enrolled in this study, of which 77 responded to NAC. Diffuse oedema, stable or increased oedema during early NAC were adverse predictors for treatment response, while a greater reduction in tumor size and increase in ADC value were favorable predictors (all P<0.05). Furthermore, 20 of 60 patients who were followed up experienced recurrence. Diffuse oedema, pre-pectoral or subcutaneous oedema, and non-concentric shrinkage patterns after NAC were risk factors for DFS, whereas a greater increase in ADC value was a protective factor. Incorporating oedema and shrinkage patterns into traditional MRI features improved the predictive performance for treatment response (AUC from 0.76-0.78 to 0.80-0.83) and DFS (C-index from 0.67-69 to 0.75-0.80).

CONCLUSIONS

Oedema is an unfavorable predictor for treatment response and survival outcomes, while shrinkage patterns contribute more to the prognostic value, both of which could offer supplementary benefits for clinical outcomes in luminal BC.

A predictive model using MRI and clinicopathologic features for breast cancer recurrence in young women treated with upfront surgery

OBJECTIVES

To identify preoperative breast MR imaging and clinicopathological variables related to recurrence and develop a risk prediction model for recurrence in young women with breast cancer treated with upfront surgery.

METHODS

This retrospective study analyzed 438 consecutive women with breast cancer aged 35 years or younger between January 2007 and December 2016. Breast MR images before surgery were independently reviewed by breast radiologists blinded to patient outcomes. The clinicopathological data including patient demographics, clinical features, and tumor characteristics were reviewed. Univariate and multivariate logistic regression analyses were used to identify the independent factors associated with recurrence. The risk prediction model for recurrence was developed, and the discrimination and calibration abilities were assessed.

RESULTS

Of 438 patients, 95 (21.7%) developed recurrence after a median follow-up of 65 months. Tumor size at MR imaging (HR = 1.158, p = 0.006), multifocal or multicentric disease (HR = 1.676, p = 0.017), and peritumoral edema on T2WI (HR = 2.166, p = 0.001) were identified as independent predictors of recurrence, while adjuvant endocrine therapy (HR = 0.624, p = 0.035) was inversely associated with recurrence. The prediction model showed good discrimination ability in predicting 5-year recurrence (C index, 0.707 in the development cohort; 0.686 in the validation cohort) and overall recurrence (C index, 0.699 in the development cohort; 0.678 in the validation cohort). The calibration plot demonstrated an excellent correlation (concordance correlation coefficient, 0.903).

CONCLUSION

A prediction model based on breast MR imaging and clinicopathological features showed good discrimination to predict recurrence in young women with breast cancer treated with upfront surgery, which could contribute to individualized risk stratification.

CLINICAL RELEVANCE STATEMENT

Our prediction model, incorporating preoperative breast MR imaging and clinicopathological features, predicts recurrence in young women with breast cancer undergoing upfront surgery, facilitating personalized risk stratification and informing tailored management strategies.

KEY POINTS

Younger women with breast cancer have worse outcomes than those diagnosed at more typical ages. The described prediction model showed good discrimination performance in predicting 5-year and overall recurrence. Incorporating better risk stratification tools in this population may help improve outcomes.

Initial experience with a next-generation low-field MRI scanner: Potential for breast imaging?

PURPOSE

Broader clinical adoption of breast magnetic resonance imaging (MRI) faces challenges such as limited availability and high procedural costs. Low-field technology has shown promise in addressing these challenges. We report our initial experience using a next-generation scanner for low-field breast MRI at 0.55T.

METHODS

This initial cases series was part of an institutional review board-approved prospective study using a 0.55T scanner (MAGNETOM Free.Max, Siemens Healthcare, Erlangen/Germany: height < 2 m, weight < 3.2 tons, no quench pipe) equipped with a seven-channel breast coil (Noras, Höchberg/Germany). A multiparametric breast MRI protocol consisting of dynamic T1-weighted, T2-weighted, and diffusion-weighted sequences was optimized for 0.55T. Two radiologists with 12 and 20 years of experience in breast MRI evaluated the examinations.

RESULTS

Twelve participants (mean age: 55.3 years, range: 36-78 years) were examined. The image quality was diagnostic in all examinations and not impaired by relevant artifacts. Typical imaging phenotypes were visualized. The scan time for a complete, non-abbreviated breast MRI protocol ranged from 10:30 to 18:40 min.

CONCLUSION

This initial case series suggests that low-field breast MRI is feasible at diagnostic image quality within an acceptable examination time.

Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma

BACKGROUND

 Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric).

METHOD

 This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality.

RESULTS AND CONCLUSION

 Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers.

KEY POINTS

· Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas..

CITATION FORMAT

· Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.

The Influence of Tumor-Specific Markers in Breast Cancer on Other Blood Parameters

BACKGROUND

Breast cancer is the most frequently diagnosed cancer among women, responsible for the highest number of cancer-related deaths worldwide. There is limited data available related to serum tumor markers in breast cancer and other blood parameters or other glandular laboratory parameters. This study aims to evaluate the correlation of tumor-specific markers for breast cancer with other blood parameters and how these correlations could impact clinical management.

MATERIAL AND METHOD

This retrospective study represents a data analysis from 1 January 2020 to 31 May 2023, in the County Hospital of Timisoara, Romania. We reviewed all the cases where, in the laboratory analyses, the serum tumor specific biomarkers for breast cancer were analyzed.

RESULTS

A statistical analysis was performed in order to identify a possible relationship between CA 15-3 and the various biomarkers and blood parameters included in the present study. Values were classified according to reference ranges. The tests revealed no statistically significant associations between CA 15-3 values and the levels of CA125 (χ2(1) = 1.852, p = 0.174), CEA (χ2(1) = 1.139, p = 0.286), AFP (Fisher's exact test, p = 0.341), fT4 (Fisher's exact test, p = 0.310), TSH (Fisher's exact test, p = 0.177), or PTH (Fisher's exact test, p = 0.650).

CONCLUSION

The findings indicate a lack of strong correlation between CA 15-3 and CA125, CEA, AFP, thyroid function markers, or PTH within this cohort.

Unveiling the Intricacies: A Comprehensive Review of Magnetic Resonance Imaging (MRI) Assessment of T2-Weighted Hyperintensities in the Neuroimaging Landscape

T2-weighted hyperintensities in neuroimaging represent areas of heightened signal intensity on magnetic resonance imaging (MRI) scans, holding crucial importance in neuroimaging. This comprehensive review explores the T2-weighted hyperintensities, providing insights into their definition, characteristics, clinical relevance, and underlying causes. It highlights the significance of these hyperintensities as sensitive markers for neurological disorders, including multiple sclerosis, vascular dementia, and brain tumors. The review also delves into advanced neuroimaging techniques, such as susceptibility-weighted and diffusion tensor imaging, and the application of artificial intelligence and machine learning in hyperintensities analysis. Furthermore, it outlines the challenges and pitfalls associated with their assessment and emphasizes the importance of standardized protocols and a multidisciplinary approach. The review discusses future directions for research and clinical practice, including the development of biomarkers, personalized medicine, and enhanced imaging techniques. Ultimately, the review underscores the profound impact of T2-weighted hyperintensities in shaping the landscape of neurological diagnosis, prognosis, and treatment, contributing to a deeper understanding of complex neurological conditions and guiding more informed and effective patient care.

Recent nanotheranostic approaches in cancer research

Humanity is suffering from cancer which has become a root cause of untimely deaths of individuals around the globe in the recent past. Nanotheranostics integrates therapeutics and diagnostics to monitor treatment response and enhance drug efficacy and safety. We hereby propose to discuss all recent cancer imaging and diagnostic tools, the mechanism of targeting tumor cells, and current nanotheranostic platforms available for cancer. This review discusses various nanotheranostic agents and novel molecular imaging tools like MRI, CT, PET, SPEC, and PAT used for cancer diagnostics. Emphasis is given to gold nanoparticles, silica, liposomes, dendrimers, and metal-based agents. We also highlight the mechanism of targeting the tumor cells, and the limitations of different nanotheranostic agents in the field of research for cancer treatment. Due to the complexity in this area, multifunctional and hybrid nanoparticles functionalized with targeted moieties or anti-cancer drugs show the best feature for theranostics that enables them to work on carrying and delivering active materials to the desired area of the requirement for early detection and diagnosis. Non-invasive imaging techniques have a specificity of receptor binding and internalization processes of the nanosystems within the cancer cells. Nanotheranostics may provide the appropriate medicine at the appropriate dose to the appropriate patient at the appropriate time.

The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses

BACKGROUND

Female breast cancer has surpassed lung cancer as the most common cancer, and is also the main cause of cancer death for women worldwide. Breast cancer <1 cm showed excellent survival rate. However, the diagnosis of minimal breast cancer (MBC) is challenging.

OBJECTIVE

The purpose of our research is to develop and validate an radiomics model based on ultrasound images for early recognition of MBC.

METHODS

302 breast masses with a diameter of <10 mm were retrospectively studied, including 159 benign and 143 malignant breast masses. The radiomics features were extracted from the gray-scale ultrasound image of the largest face of each breast mass. The maximum relevance minimum reduncancy and recursive feature elimination methods were used to screen. Finally, 10 features with the most discriminating value were selected for modeling. The random forest was used to establish the prediction model, and the rad-score of each mass was calculated. In order to evaluate the effectiveness of the model, we calculated and compared the area under the curve (AUC) value, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the model and three groups with different experience in predicting small breast masses, and drew calibration curves and decision curves to test the stability and consistency of the model.

RESULTS

When we selected 10 radiomics features to calculate the rad-score, the prediction efficiency was the best, the AUC values for the training set and testing set were 0.840 and 0.793, which was significantly better than the insufficient experience group (AUC = 0.673), slightly better than the moderate experience group (AUC = 0.768), and was inferior to the experienced group (AUC = 0.877). The calibration curve and decision curve also showed that the radiomics model had satisfied stability and clinical application value.

CONCLUSION

The radiomics model based on ultrasound image features has a satisfied predictive ability for small breast masses, and is expected to become a potential tool for the diagnosis of MBC, and it is a zero cost (in terms of patient participation and imaging time).

MRI phenotypes associated with breast cancer predisposing genetic variants, a multisite review

PURPOSE

Examine MRI phenotypes of breast cancers arising in patients with various pathogenic variants, to assess for imaging trends and associations.

METHOD

Multisite retrospective review evaluated 410 patients from 2001 to 2020 with breast cancer and a predisposing pathogenic variant who underwent breast MRI at time of cancer diagnosis. Dominant malignant lesion features were reported, including lesion type (mass versus non-mass enhancement), size, shape, margin, internal enhancement pattern, plus other features. Kruskal-Wallis test, Fisher's exact test, and pairwise comparisons performed comparing imaging manifestations for the most frequent genetic results.

RESULTS

BRCA1 (29.5 %) and BRCA2 (25.9 %) variants were most common, followed by CHEK2 (16.6 %), ATM (8.0 %), and PALB2 (6.3 %), with significant associated differences in race/ethnicity (p = 0.040), age at cancer diagnosis (p = 0.005), tumor shapes (p = 0.001), margins (p < 0.001), grade (p < 0.001), internal enhancement pattern (rim enhancement) (p < 0.001), kinetics (washout) (p < 0.001), and presence of necrosis (p < 0.001). CHEK2 and ATM tumors were often lower grade with spiculated margins (CHEK2: 47.1 %, ATM: 45.5 %), rarely exhibiting washout or tumor necrosis (p < 0.001), and were mostly comprised of luminal molecular subtypes (CHEK2: 88.2 %, ATM: 90.9 %). BRCA1 tumors had the highest proportions with round shape (31.4 %), circumscribed margins (24.0 %), rim enhancement (24.0 %), washout (58.7 %), and necrosis (19.8 %), with 47.9 % comprised of triple negative subtype. Bilateral mastectomy was performed in higher proportions of patients with BRCA1 (84.3 %) and BRCA2 (75.5 %) variants compared to others.

CONCLUSIONS

Genetic and molecular profiles of breast cancers demonstrate reproducible MRI phenotypes.