Advances in the Molecular Landscape of Lung Cancer Brain Metastasis

Souza, Vanessa das Graças Pereira de; Araújo, Rachel Paes de; Santesso, Mariana R.; Seneda, Ana Laura; Minutentag, Iael W.; Felix, Tainara F.; Filho, Pedro Tadao Hamamoto; Pewarchuk, Michelle E.; Brockley, Liam J.; Marchi, Fábio Albuquerque; Lam, Wan L.; Drigo, Sandra A.; Reis, Patrícia P.

doi:10.3390/cancers15030722

Cited by 12 publications

(15 citation statements)

References 216 publications

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“…A predictive model that included radiomics, histology, lymph node involvement, and tumor grade was shown to have a maximal sensitivity of ~70% and specificity of ~60% for the prediction of subsequent brain metastases, far below our results [19]. Most studies have focused on detecting brain metastases at the earliest possible time through sensitive blood or imaging tests [20–23]. However, these studies do not address the prediction of the development of subsequent brain metastases at the time of initial diagnosis.…”

Section: Discussionmentioning

confidence: 72%

AI‐guided histopathology predicts brain metastasis in lung cancer patients

Zhou,

Watson,

Bernadt

et al. 2024

The Journal of Pathology

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Brain metastases can occur in nearly half of patients with early and locally advanced (stage I–III) non‐small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E‐stained primary tumor tissue sections from stage I–III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I–III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met−, 93 patients) were subjected to whole‐slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met−) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met−) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL‐based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL‐based algorithms using routine H&E‐stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow‐up and may thus be spared systemic therapy. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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Section: Discussionmentioning

confidence: 72%

AI‐guided histopathology predicts brain metastasis in lung cancer patients

Zhou,

Watson,

Bernadt

et al. 2024

The Journal of Pathology

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“…Another possible reason might be that more efforts have been made in recent years aiming to improve the prognosis of brain. 8,[10][11][12][13][14][15][16][17][18][19] In this study, patients with liver metastasis had the poorest prognosis among all single-organ metastasis and were similar to those with MOM. Our results are consistent with the previous studies.…”

Section: Discussionmentioning

confidence: 87%

“…This might be the possible reason why patients' prognosis for those suffering from bone metastasis is poorer than those with brain metastasis. Another possible reason might be that more efforts have been made in recent years aiming to improve the prognosis of brain 8,[10][11][12][13][14][15][16][17][18][19] .…”

Section: Accepted Manuscriptmentioning

confidence: 99%

The Incidence and Effect of Different Organ Metastasis on the Prognosis of NSCLC

Wei

et al. 2023

Thorac Cardiovasc Surg

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Objective: The aim of this study was to explore the effect of different organ metastasis on the prognosis of NSCLC. Methods: Patients with distant metastatic NSCLC were selected from Surveillance, Epidemiology, and End Results (SEER) database during 2016-2019. The incidence of different organ metastasis and their association with clinicopathological factors were explored. Overall survival (OS) and lung cancer-specific survival (LCSS) for metastatic NSCLC were calculated and multivariate Cox regression analysis was performed with a nomogram for OS being constructed based on Cox regression. Results: Total 26,210 patients with distant metastatic NSCLC were included in this study. Around 48.9% of the metastatic NSCLC were multiple-organ metastasis and bone was the most commonly involved organ (44.4%). For patients with single-organ metastasis, the prognosis for lung or distant LNs metastasis was better than others (with median OS of 15 and 16 months for lung and distant LNs metastasis respectively), and liver metastasis resulted in the worst prognosis with median OS of 8 months. A nomogram was constructed to visualize Cox regression model, along with the receiver operating characteristic (ROC) curve demonstrated good discrimination for the predictive model with 1- and 2-year area under the curve of ROC of 0.687 and 0.702, respectively. Conclusion: The prognosis of NSCLC patients with distant metastasis was poor. Liver metastasis results in the worst prognosis among the single organ metastasis. The nomogram developed based on the Cox regression model has provided a useful tool to estimate the probability of OS of the metastatic NSCLC.

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“…Metastasis is a complex process by which cancer cells spread from the primary tumor to distant sites in the body, such as the bones, liver, or brain [5][6][7]. Cancer-related deaths are overwhelmingly the result of this disease progression (>90%), where metastasis leads to the impairment of vital organ functions [8,9].…”

Section: Introductionmentioning

confidence: 99%

Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis

Souza

Forder²,

Brockley³

et al. 2023

IJMS

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Liquid biopsies have emerged as a promising tool for the detection of metastases as well as local and regional recurrence in lung cancer. Liquid biopsy tests involve analyzing a patient’s blood, urine, or other body fluids for the detection of biomarkers, including circulating tumor cells or tumor-derived DNA/RNA that have been shed into the bloodstream. Studies have shown that liquid biopsies can detect lung cancer metastases with high accuracy and sensitivity, even before they are visible on imaging scans. Such tests are valuable for early intervention and personalized treatment, aiming to improve patient outcomes. Liquid biopsies are also minimally invasive compared to traditional tissue biopsies, which require the removal of a sample of the tumor for further analysis. This makes liquid biopsies a more convenient and less risky option for patients, particularly those who are not good candidates for invasive procedures due to other medical conditions. While liquid biopsies for lung cancer metastases and relapse are still being developed and validated, they hold great promise for improving the detection and treatment of this deadly disease. Herein, we summarize available and novel approaches to liquid biopsy tests for lung cancer metastases and recurrence detection and describe their applications in clinical practice.

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Advances in the Molecular Landscape of Lung Cancer Brain Metastasis

Cited by 12 publications

References 216 publications

AI‐guided histopathology predicts brain metastasis in lung cancer patients

AI‐guided histopathology predicts brain metastasis in lung cancer patients

The Incidence and Effect of Different Organ Metastasis on the Prognosis of NSCLC

Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis

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