Imaging tests in staging and surveillance of non-metastatic breast cancer: changes in routine clinical practice and cost implications

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.

An Online Calculator for Estimating Breast Implant Volume from Imaging

Breast implant surgery remains one of the most common surgical procedures performed in the United States. Implant exchange can be complicated by unavailability of medical records or implant identification cards. Using chest imaging of 154 breast implants, an algorithm for estimating breast implant volume was generated. Based on four simple measurements and patient body mass index, a free, online calculator was created with a mean error of volume estimate of less than 1 cm3 and a SD of 44 cm3. In instances where a surgeon does not have implant records available but has chest imaging, this online tool can be used to obtain a relatively accurate estimate of implant volume.

Economic assessment of molecular imaging in the oncology treatment process

The development towards targeted treatments in oncology has been accompanied by significant improvements in molecular imaging. Yet, broad application of novel imaging techniques has partly been slowed down due to economical considerations. Building on the broad positive evidence of its diagnostic accuracy, modelling of effects on long-term costs and effectiveness may help to foster a broader application and acceptance of comprehensive molecular imaging techniques, such as PET/MRI. In this article, common economic evaluation techniques and cost-effectiveness analysis (CEA) evaluation methods will be introduced including Markov models and incremental cost-effectiveness ratios (ICER). This is complemented with a review of literature on recently published cost-effectiveness of molecular imaging. Additionally, the strategic relevance of CEAs for the molecular imaging community is discussed and combined with a global outlook.

The lingering mysteries of metastatic recurrence in breast cancer

Despite being the hallmark of cancer that is responsible for the highest number of deaths, very little is known about the biology of metastasis. Metastatic disease typically manifests after a protracted period of undetectable disease following surgery or systemic therapy, owing to relapse or recurrence. In the case of breast cancer, metastatic relapse can occur months to decades after initial diagnosis and treatment. In this review, we provide an overview of the known key factors that influence metastatic recurrence, with the goal of highlighting the critical unanswered questions that still need to be addressed to make a difference in the mortality of breast cancer patients.

Discrepancies of current recommendations in breast cancer follow-up: a systematic review

INTRODUCTION

Management and optimal follow-up of early breast cancer survivors remain up to this day a challenge due to the lack of well-established guidelines. Multiple medical societies, organizations and working groups have provided recommendations for follow-up but there is no uniform, globally approved algorithm to guide clinical practice.

METHODS

A systematic review was performed to identify and evaluate discrepancies between available guidelines for the follow-up of breast cancer survivors.

RESULTS

Differences in the follow-up schedule, laboratory and imaging investigations were noted. In the clinical practice setting, the situation is complicated further by clinicians who often request unnecessary tests not currently incorporated in any of the existing guidelines.

CONCLUSIONS

Follow-up of patients with early breast cancer needs to become standardized and prospective clinical trials focusing on optimal follow-up are more than mandatory.

Sensitivity and specificity of breast cancer ICD-9-CM codes in three Italian administrative healthcare databases: a diagnostic accuracy study

OBJECTIVES

To assess the accuracy of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes in identifying patients diagnosed with incident carcinoma in situ and invasive breast cancer in three Italian administrative databases.

DESIGN

A diagnostic accuracy study comparing ICD-9-CM codes for carcinoma in situ (233.0) and for invasive breast cancer (174.x) with medical chart (as a reference standard). Case definition: (1) presence of a primary nodular lesion in the breast and (2) cytological or histological documentation of cancer from a primary or metastatic site.

SETTING

Administrative databases from Umbria Region, Azienda Sanitaria Locale (ASL) Napoli 3 Sud (NA) and Friuli VeneziaGiulia (FVG) Region.

PARTICIPANTS

Women with breast carcinoma in situ (n=246) or invasive breast cancer (n=384) diagnosed (in primary position) between 2012 and 2014.

OUTCOME MEASURES

Sensitivity and specificity for codes 233.0 and 174.x.

RESULTS

For invasive breast cancer the sensitivities were 98% (95% CI 93% to 99%) for Umbria, 96% (95% CI 91% to 99%) for NA and 100% (95% CI 97% to 100%) for FVG. Specificities were 90% (95% CI 82% to 95%) for Umbria, 91% (95% CI 83% to 96%) for NA and 91% (95% CI 84% to 96%) for FVG.For carcinoma in situ the sensitivities were 100% (95% CI 93% to 100%) for Umbria, 100% (95% CI 95% to 100%) for NA and 100% (95% CI 96% to 100%) for FVG. Specificities were 98% (95% CI 93% to 100%) for Umbria, 86% (95% CI 78% to 92%) for NA and 90% (95% CI 82% to 95%) for FVG.

CONCLUSIONS

Administrative healthcare databases from Umbria, NA and FVG are accurate in identifying hospitalised news cases of carcinoma of the breast. The proposed case definition is a powerful tool to perform research on large populations of newly diagnosed patients with breast cancer.

Population-based validation of the National Cancer Comprehensive Network recommendations for breast cancer staging

OBJECTIVE

The aim of the current study is to evaluate the performance characteristics of the National Comprehensive Cancer Network (NCCN) staging recommendations for breast cancer with regard to the detection of lung, bone, and liver metastases.

METHODS

Surveillance, epidemiology, and end points (SEER) database (2010-2015) was accessed, and patients with breast cancer and complete information about T stage and clinical N stage, ER status, Her2 status, and metastatic sites were extracted. Performance characteristics evaluated for the current study included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), number needed to investigate (NNI), and accuracy.

RESULTS

A total of 239,196 patients were included in the analysis. For the overall cohort, the required PPV (for the recognition of lung metastases) is 10.6% and NNI to detect one case of lung metastasis is 9.4. Likewise, PPV (for the recognition of bone metastases) is 18.6% and NNI to detect one case of bone metastasis is 5.3. Moreover, PPV (for the recognition of liver metastases) is 7.6% and NNI to detect one case of liver metastasis is 13.1. When changing the threshold for baseline imaging to includeT2N1 patients, a better balance between sensitivity and specificity among ER+/Her2- patients (> 92% for both sensitivity and specificity for the three metastatic sites) was observed. On the other hand, the proposed change improved sensitivity while it lowers significantly the specificity among Her2+ and triple negative subtypes (specificity < 84% for Her2+ disease for the three metastatic sites; specificity < 87% for triple negative disease for the three metastatic sites).

CONCLUSION

The current NCCN recommendations for breast cancer staging have an excellent NPV and miss only few patients with lung, liver, or bone metastases. Future studies incorporating the subtype of breast cancer as a determinant of staging pathway is needed.

Effect of Mesoporous Nano Water Reservoir on MR Relaxivity

In the present work, an attempt was made to engineer a mesoporous silica coated magnetic nanoparticles (MNF@mSiO₂) for twin mode contrast in magnetic resonance imaging (MRI) with reduced toxicity. Superparamagnetic manganese ferrite nanoparticles were synthesized with variable mesoporous silica shell thickness to control the water molecules interacting with metal oxide core. 178 nm was the optimum hydrodynamic diameter of mesoporous ferrite core-shell nanoparticles that showed maximum longitudinal relaxation time (T1) and transverse relaxation time (T2) in MRI due to the storage of water molecules in mesoporous silica coating. Besides the major role of mesoporous silica in controlling relaxivity, mesoporous silica shell also reduces the toxicity and enhances the bioavailability of superparamagnetic manganese ferrite nanoparticles. The in vitro toxicity assessment using HepG2 liver carcinoma cells shows that the mesoporous silica coating over ferrite nanoparticles could exert less toxicity compared to the uncoated particle.