Growth rate of newly developed metastatic brain tumors after thoracotomy in patients with non-small cell lung cancer

Mathematical modeling of brain metastases growth and response to therapies: A review

Brain metastases (BMs) are the most common intracranial tumor type and a significant health concern, affecting approximately 10% to 30% of all oncological patients. Although significant progress is being made, many aspects of the metastatic process to the brain and the growth of the resulting lesions are still not well understood. There is a need for an improved understanding of the growth dynamics and the response to treatment of these tumors. Mathematical models have been proven valuable for drawing inferences and making predictions in different fields of cancer research, but few mathematical works have considered BMs. This comprehensive review aims to establish a unified platform and contribute to fostering emerging efforts dedicated to enhancing our mathematical understanding of this intricate and challenging disease. We focus on the progress made in the initial stages of mathematical modeling research regarding BMs and the significant insights gained from such studies. We also explore the vital role of mathematical modeling in predicting treatment outcomes and enhancing the quality of clinical decision-making for patients facing BMs.

Time interval from diagnosis to treatment of brain metastases with stereotactic radiosurgery is not associated with radionecrosis or local failure

Introduction

Brain metastases are the most common intracranial tumor diagnosed in adults. In patients treated with stereotactic radiosurgery, the incidence of post-treatment radionecrosis appears to be rising, which has been attributed to improved patient survival as well as novel systemic treatments. The impacts of concomitant immunotherapy and the interval between diagnosis and treatment on patient outcomes are unclear.

Methods

This single institution, retrospective study consisted of patients who received single or multi-fraction stereotactic radiosurgery for intact brain metastases. Exclusion criteria included neurosurgical resection prior to treatment and treatment of non-malignant histologies or primary central nervous system malignancies. A univariate screen was implemented to determine which factors were associated with radionecrosis. The chi-square test or Fisher’s exact test was used to compare the two groups for categorical variables, and the two-sample t-test or Mann-Whitney test was used for continuous data. Those factors that appeared to be associated with radionecrosis on univariate analyses were included in a multivariable model. Univariable and multivariable Cox proportional hazards models were used to assess potential predictors of time to local failure and time to regional failure.

Results

A total of 107 evaluable patients with a total of 256 individual brain metastases were identified. The majority of metastases were non-small cell lung cancer (58.98%), followed by breast cancer (16.02%). Multivariable analyses demonstrated increased risk of radionecrosis with increasing MRI maximum axial dimension (OR 1.10, p=0.0123) and a history of previous whole brain radiation therapy (OR 3.48, p=0.0243). Receipt of stereotactic radiosurgery with concurrent immunotherapy was associated with a decreased risk of local failure (HR 0.31, p=0.0159). Time interval between diagnostic MRI and first treatment, time interval between CT simulation and first treatment, and concurrent immunotherapy had no impact on incidence of radionecrosis or regional failure.

Discussion

An optimal time interval between diagnosis and treatment for intact brain metastases that minimizes radionecrosis and maximizes local and regional control could not be identified. Concurrent immunotherapy does not appear to increase the risk of radionecrosis and may improve local control. These data further support the safety and synergistic efficacy of stereotactic radiosurgery with concurrent immunotherapy.

GammaTile® (GT) as a brachytherapy platform for rapidly growing brain metastasis

Background

A subset of brain metastasis (BM) shows rapid recurrence post-initial resection or aggressive tumor growth between interval scans. Here we provide a pilot experience in the treatment of these BM with GammaTile® (GT), a collagen tile-embedded Cesium 131 (¹³¹Cs) brachytherapy platform.

Methods

We identified ten consecutive patients (2019-2023) with BM that showed either (1) symptomatic recurrence while awaiting post-resection radiosurgery or (2) enlarged by >25% of tumor volume on serial imaging and underwent surgical resection followed by GT placement. Procedural complication, 30-day readmission, local control, and overall survival were assessed.

Results

For this cohort of ten BM patients, 3 patients suffered tumor progression while awaiting radiosurgery and 7 showed >25% tumor growth prior to surgery and GT placement. There were no procedural complications or 30-day mortality. All patients were discharged home, with a median hospital stay of 2 days (range: 1-9 days). 4/10 patients experienced symptomatic improvement while the remaining patients showed stable neurologic conditions. With a median follow-up of 186 days (6.2 months, range: 69-452 days), no local recurrence was detected. The median overall survival (mOS) for the newly diagnosed BM was 265 days from the time of GT placement. No patients suffered from adverse radiation effects.

Conclusion

Our pilot experience suggests that GT offers favorable local control and safety profile in patients suffering from brain metastases that exhibit aggressive growth patterns and support the future investigation of this treatment paradigm.

Evaluating the natural growth rate of metastatic cancer to the brain

BACKGROUND

Brain metastases are becoming increasingly more prevalent as cancer patients survive longer with both improved local and systemic therapy. Little is known, however, of the natural growth rates of brain metastases. This investigation aims to ascertain this growth rate of these lesions before the initiation of any CNS- directed therapy.

METHODS

A total of 700 patients were screened, identifying 18 cancer patients (13 breast and 5 lung) with 29 brain metastases that were serially imaged from 2011 to 2017 before treatment for their intracranial metastases. Growth rates were measured by contouring lesions serially across at least two MRI studies in iPlan software by independent raters. These values were then compared between primary (breast and lung) cancer cohorts.

RESULTS

The mean age at diagnosis was 53 and 95% were female. The interval between primary cancer diagnosis and brain metastases was 4.6 years and 1.2 years in the breast and lung cancer groups, respectively. Of the breast and lung cancer patients, 23% and 40% were deceased, with respective 5.08 cm3 and 2.44 cm3 initial tumor volumes. The average growth rate of lung and breast tumors was 0.018 and 0.040 cm3/day, respectively, with deceased patients having larger and faster growing tumors. Breast and lung metastases grew 2.39% and 1.14% of their total volumes daily and doubling times were 86 and 139 days, respectively.

CONCLUSION

This investigation provides a unique perspective into the biological growth of metastatic brain lesions. It is our hope that this study guides timing of treatment and informs both clinicians and patients of tumor growth kinetics before initiating treatment for intracranial metastases.

Deep-Learning Detection of Cancer Metastases to the Brain on MRI

BACKGROUND

Approximately one-fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, planning of radiotherapy, and the monitoring of treatment response. Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective.

PURPOSE

To develop a deep-learning-based approach for finding brain metastasis on MRI.

STUDY TYPE

Retrospective.

SEQUENCE

Axial postcontrast 3D T₁ -weighted imaging.

FIELD STRENGTH

1.5T and 3T.

POPULATION

A total of 361 scans of 121 patients were used to train and test the Faster region-based convolutional neural network (Faster R-CNN): 1565 lesions in 270 scans of 73 patients for training; 488 lesions in 91 scans of 48 patients for testing. From the 48 outputs of Faster R-CNN, 212 lesions in 46 scans of 18 patients were used for training the RUSBoost algorithm (MatLab) and 276 lesions in 45 scans of 30 patients for testing.

ASSESSMENT

Two radiologists diagnosed and supervised annotation of metastases on brain MRI as ground truth. This data were used to produce a 2-step pipeline consisting of a Faster R-CNN for detecting abnormal hyperintensity that may represent brain metastasis and a RUSBoost classifier to reduce the number of false-positive foci detected.

STATISTICAL TESTS

The performance of the algorithm was evaluated by using sensitivity, false-positive rate, and receiver's operating characteristic (ROC) curves. The detection performance was assessed both per-metastases and per-slice.

RESULTS

Testing on held-out brain MRI data demonstrated 96% sensitivity and 20 false-positive metastases per scan. The results showed an 87.1% sensitivity and 0.24 false-positive metastases per slice. The area under the ROC curve was 0.79.

CONCLUSION

Our results showed that deep-learning-based computer-aided detection (CAD) had the potential of detecting brain metastases with high sensitivity and reasonable specificity.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1227-1236.

The Efficacy of Postoperative Chemotherapy for Patients with Metastatic Brain Tumors from Non-Small Cell Lung Cancer

BACKGROUND

The purpose of this study is to evaluate the effect of postoperative chemotherapy on recurrence and survival in patients after resection of metastatic brain tumors from non-small cell lung cancers.

METHODS

Patients who went through resection of a single metastatic brain tumor from non-small cell lung cancer from July 2001 to December 2012 were reviewed. Those selected were 77 patients who survived more than 3 months after surgery were selected. Among them, 44 patients received various postoperative systemic chemotherapies, 33 patients received postoperative adjuvant whole brain radiotherapy (WBRT). Local/distant recurrence rate, local/distant recurrence free survival, disease free survival (DFS), and overall survival were compared between the two groups.

RESULTS

Among the 77 patients, there were 19 (24.7%) local recurrences. Local recurrence occurred in 7 (21.2%) of 33 patients in the adjuvant radiotherapy (RT) group and in 12 (27.3%) of the 44 patients in the chemotherapy group (p=0.542). Among the 77 patients, there were 34 (44.1%) distant recurrences. Distant recurrence occurred in 7 (21.2%) of the 33 patients in the adjuvant RT group and in 27 (61.4%) of the 44 patients in the chemotherapy group (p<0.0005). Patients' survival in terms of local recurrence free survival, distant recurrence free survival, DFS, and overall survival was not shown to be statistically different between the two groups before and after adjusting for covariates.

CONCLUSION

There was no significant difference observed between postoperative adjuvant chemotherapy and adjuvant WBRT in terms of patients' survival. Postoperative chemotherapy is more feasible and may be an appropriate option for simultaneous control of both primary and metastatic lesions.