Beatriz Asenjo’s research while affiliated with Hospital Regional Universitario de Málaga and other places

Background Stereotactic radiotherapy is the preferred treatment for managing patients with fewer than five brain metastases (BMs). However, some lesions recur after irradiation. The purpose of this study was to identify patients who are at a higher risk of failure, which can help in adjusting treatments and preventing recurrence. Methods In this retrospective multicenter study, we analyzed the predictive significance of a set of interpretable morphological features derived from contrast-enhanced (CE) T1-weighted MR images as imaging biomarkers using Kaplan–Meier analysis. The feature sets studied included the total and necrotic volumes, the surface regularity and the CE rim width. Additionally, we evaluated other nonmorphological variables and performed multivariate Cox analysis. Results A total of 183 lesions in 128 patients were included (median age 61 [31–95], 64 men and 64 women) treated with stereotactic radiotherapy (57% single fraction, 43% fractionated radiotherapy). None of the studied variables measured at diagnosis were found to have prognostic value. However, the total and necrotic volumes and the CE rim width measured at the first follow-up after treatment and the change in volume due to irradiation can be used as imaging biomarkers for recurrence. The optimal classification was achieved by combining the changes in tumor volume before and after treatment with the presence or absence of necrosis (p < < 0.001). Conclusion This study demonstrated the prognostic significance of interpretable morphological features extracted from routine clinical MR images following irradiation in brain metastases, offering valuable insights for personalized treatment strategies. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-024-00753-0.

Background. Stereotactic radiotherapy is the preferred treatment for managing patients with fewer than five brain metastases (BMs). However, some lesions recur after irradiation. The purpose of this study was to identify patients who are at a higher risk of failure, which can help in adjusting treatments and preventing recurrence. Methods. In this retrospective multicenter study, we analyzed the predictive significance of a set of interpretable morphological features derived from T1-weighted MR images, as imaging biomarkers, using Kaplan-Meier estimators. The feature set studied included the total and necrotic volumes, the surface regularity and the CE rim width. Additionally, we evaluated other non-morphological variables and performed multivariate cox analysis. Results. A total of 183 lesions in 128 patients were included (median age 61 [31-95], 64 men and 64 women). None of the studied variables measured at diagnosis were found to have prognostic value. However, the total and necrotic volumes and the CE rim width measured at the first follow-up after treatment and the change in volumes due to irradiation can be used as biomarkers for recurrence. Optimal classification was achieved when combining volume changes before and after treatment with the presence or absence of necrosis (p<<0.001). Conclusion. This study demonstrates the prognostic significance of interpretable morphological features extracted from routine clinical MR imaging following irradiation in brain metastases, offering valuable insights for personalized treatment strategies.

Background The Response Assessment in Neuro-Oncology for Brain Metastases (RANO-BM) criteria are the gold standard for assessing brain metastases (BMs) treatment response. However, they are limited by their reliance on one dimension, despite the routine use of high-resolution T1-weighted MRI scans for BMs, which allows for 3D measurements. Our study aimed to investigate whether volumetric measurements could improve the response assessment in patients with BMs. Methods We retrospectively evaluated a dataset comprising 783 BMs and analyzed the response of 185 of them from 132 patients who underwent stereotactic radiotherapy between 2007 and 2021 at five hospitals. We used T1-weighted MRIs to compute the volume of the lesions. For the volumetric criteria, progressive disease was defined as at least a 30% increase in volume, and partial response was characterized by a 20% volume reduction. Results Our study showed that the proposed volumetric criteria outperformed the RANO-BM criteria in several aspects: 1) Evaluating every lesion, while RANO-BM failed to evaluate 9.2% of them. 2) Classifying response effectively in 140 lesions, compared to only 72 lesions classified by RANO-BM. 3) Identifying BM recurrences a median of 3.3 months earlier than RANO-BM criteria. Conclusion Our study demonstrates the superiority of volumetric criteria in improving the response assessment of BMs compared to the RANO-BM criteria. Our proposed criteria allow for evaluation of every lesion, regardless of its size or shape, better classification, and enable earlier identification of progressive disease. Volumetric criteria provide a standardized, reliable, and objective tool for assessing treatment response.

Tumor growth is the result of the interplay of complex biological processes in huge numbers of individual cells living in changing environments. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or simple animal models with bounded-growth dynamics accurately. However, results for the growth of human cancers in patients are scarce. Our study mined a large dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up to find growth laws for untreated BMs and recurrent treated BMs. Untreated BMs showed high growth exponents, most likely related to the underlying evolutionary dynamics, with experimental tumors in mice resembling accurately the disease. Recurrent BMs growth exponents were smaller, most probably due to a reduction in tumor heterogeneity after treatment, which may limit the tumor evolutionary capabilities. In silico simulations using a stochastic discrete mesoscopic model with basic evolutionary dynamics led to results in line with the observed data.

Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability.

Background Radiation necrosis (RN) is a frequent adverse event after fractionated stereotactic radiotherapy (FSRT) or single-session stereotactic radiosurgery (SRS) treatment of brain metastases (BMs). It is difficult to distinguish RN from progressive disease (PD) due to their similarities on the magnetic resonance images (MRIs). Previous theoretical studies have hypothesized that RN could have a faster, although transient, growth dynamics after FSRT/SRS, but no study has proven that hypothesis using patient data. Thus, we hypothesized that lesion size time dynamics obtained from growth laws fitted with data from sequential volumetric measurements on MRIs may help in discriminating recurrent BMs from RN events. Methods A total of 101 BMs from different institutions, growing after FSRT/SRS (60 PDs and 41 RNs) in 86 patients, displaying growth for at least three consecutive MRI follow-ups were selected for the study from a database of 1031 BMs. The three parameters of the Von Bertalanffy growth law were determined for each BM and used to discriminate statistically PDs from RNs. Results Growth exponents in patients with RNs were found to be substantially larger than those of PD, due to the faster, although transient, dynamics of inflammatory processes. Statistically significant differences (p < 0.001) were found between both groups. The ROC curve (AUC = 0.76) supported the ability of the growth law exponent to classify the events. Conclusions Growth law exponents obtained from sequential longitudinal MRIs after FSRT/SRS can be used as a complementary tool in the differential diagnosis between RN and PD.

Tumor growth is the result of the interplay of complex biological processes in a huge number of individual cells in a changing environment. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or in animal models with bounded-growth dynamics accurately. However, results for human cancers in patients are scarce. The study mined a dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up, treated with radiosurgery (SRS) to find growth laws for untreated BMs, relapsing treated BMs, and radiation necrosis (RN). Untreated BMs showed sustained growth acceleration, most likely related to the underlying evolutionary dynam- ics. Relapsing BM growth was slower, most probably due to a reduction in tumor heterogeneity after SRS, which may limit the evolutionary possibilities of the tumor. RN lesions had significantly larger growth exponents than relapsing BMs, providing a way to differentiate them from true pro- gression. This may help in solving a problem of clinical relevance, since the first condition may resolve spontaneously, and not require further work-up, while the second requires therapeutic action.

Background: MRI is assumed to be valid for distinguishing metastatic vertebral fractures (MVFs) from osteoporotic vertebral fractures (OVFs). This study assessed (1) concordance between the image-based diagnosis of MVF versus OVF and the reference (biopsy or follow-up of .6 months), (2) interobserver and intraobserver agreement on key imaging findings and the diagnosis of MVF versus OVF, and (3) whether disclosing a patient's history of cancer leads to variations in diagnosis, concordance, or agreement. Patients and Methods: This retrospective cohort study included clinical data and imaging from 203 patients with confirmed MVF or OVF provided to 25 clinicians (neurosurgeons, radiologists, orthopedic surgeons, and radiation oncologists). From January 2018 through October 2018, the clinicians interpreted images in conditions as close as possible to routine practice. Each specialist assessed data twice, with a minimum 6-week interval, blinded to assessments made by other clinicians and to their own previous assessments. The kappa statistic was used to assess interobserver and intraobserver agreement on key imaging findings, diagnosis (MVF vs OVF), and concordance with the reference. Subgroup analyses were based on clinicians' specialty, years of experience, and complexity of the hospital where they worked. Results: For diagnosis of MVF versus OVF, interobserver agreement was fair, whereas intraobserver agreement was substantial. Only the latter improved to almost perfect when a patient's history of cancer was disclosed. Interobserver agreement for key imaging findings was fair or moderate, whereas intraobserver agreement on key imaging findings was moderate or substantial. Concordance between the diagnosis of MVF versus OVF and the reference was moderate. Results were similar regardless of clinicians' specialty, experience, and hospital category. Conclusions: When MRI is used to distinguish MVF versus OVF, interobserver agreement and concordance with the reference were moderate. These results cast doubt on the reliability of basing such a diagnosis on MRI in routine practice.