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

Yoo, Heon; Jung, Eun Joo; Nam, Byung Ho; Shin, Sang Hoon; Gwak, Ho Shin; Kim, Moon Soo; Zo, Jae Ill; Lee, Jun Young

doi:10.1016/j.lungcan.2010.05.013

Cited by 8 publications

(7 citation statements)

References 19 publications

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“…In this study, we demonstrated that lung cancer metastases grew at the same rate as a previously demonstrated in an independent prospective study. [ 24 ] This helps validate previously reported data and the potential to use this data to reliably guide therapy in the future. Surprisingly, we demonstrated that breast cancer metastases grew significantly faster than lung cancer metastases, related to tumor biology but also contributed by cystic fluid production.…”

Section: Discussionsupporting

confidence: 65%

“…[ 4 ] The current lung data are much more consistent with the previously reported median time of diagnosis of 400 days or 1.10 years after primary tumor identification. [ 24 ] Expectedly, the deceased patients had larger initial tumor sizes, which likely took longer to be identified and treated.…”

Section: Discussionmentioning

confidence: 99%

“…which demonstrated the feasibility of the approach taken in this study was 0.01 cm 3 /day, similar to the 0.018 cm 3 /day rate obtained in the present cohort of lung cancer patients. [ 24 , 25 ] The previous study had been conducted after all the tumors development of chemoresistance, which was assumed to be similar to natural growth. Since our population was completely untreated, they may have had a slightly faster growth rate.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the myriad reports that document growth rates of brain metastases after systemic chemotherapy and local radiotherapy, few have studied natural growth rates before the initiation of therapy. [ 24 , 25 ] A prospective trial delaying therapy in growing lesions would be unethical, however for various reasons, patients may undergo serial imaging studies with delayed treatment. This scenario, while rare, provides an opportunity to study the natural growth rates of metastatic lesions, unaltered by any anti-tumor treatments, as is the goal of the current study.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the natural growth rate of metastatic cancer to the brain

Kobets

Backus

Fluss

et al. 2020

Surgical Neurology International

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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.

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Section: Discussionsupporting

confidence: 65%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the natural growth rate of metastatic cancer to the brain

Kobets

Backus

Fluss

et al. 2020

Surgical Neurology International

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“…In our cases series, 70% of patients died of primary cancer progression, and we had also reported previously the growth rate of metastatic brain tumor from NSCLC. The volume doubling time was about 3 months [ 22 23 ]. After resection of metastatic brain tumors, we usually followed up patients with brain MRI or CT every 3 months, and since most of the local or distant brain metastases appeared as small asymptomatic lesions, administration of radiation therapy after brain disease progression is discovered may also be effective.…”

Section: Discussionmentioning

confidence: 99%

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

Hong

Joo

Shin

et al. 2015

Brain Tumor Res Treat

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BackgroundThe 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.MethodsPatients 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.ResultsAmong 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.ConclusionThere 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.

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

Zhang

Young

Chen

et al. 2020

Magnetic Resonance Imaging

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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.

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

Cited by 8 publications

References 19 publications

Evaluating the natural growth rate of metastatic cancer to the brain

Evaluating the natural growth rate of metastatic cancer to the brain

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

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

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