Non-small cell lung cancer brain metastasis screening in the era of positron emission tomography-CT staging: Current practice and outcomes

Blood Neurofilament Light Chain and Glial Fibrillary Acidic Protein as Promising Screening Biomarkers for Brain Metastases in Patients with Lung Cancer

The diagnosis of brain metastases (BMs) in patients with lung cancer (LC) predominantly relies on magnetic resonance imaging (MRI), a method that is constrained by high costs and limited accessibility. This study explores the potential of serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) as screening biomarkers for BMs in LC patients. We conducted a retrospective analysis of 700 LC cases at the National Cancer Center, Korea, from July 2020 to June 2022, measuring sNfL and sGFAP levels at initial LC diagnosis. The likelihood of BM was evaluated using multivariate analysis and a predictive nomogram. Additionally, we prospectively monitored 177 samples from 46 LC patients initially without BM. Patients with BMs (n= 135) had significantly higher median sNfL (52.5 pg/mL) and sGFAP (239.2 pg/mL) levels compared to those without BMs (n = 565), with medians of 17.8 pg/mL and 141.1 pg/mL, respectively (p < 0.001 for both). The nomogram, incorporating age, sNfL, and sGFAP, predicted BM with an area under the curve (AUC) of 0.877 (95% CI 0.84-0.914), showing 74.8% sensitivity and 83.5% specificity. Over nine months, 93% of samples from patients without BM remained below the cutoff, while all patients developing BMs showed increased levels at detection. A nomogram incorporating age, sNfL, and sGFAP provides a valuable tool for identifying LC patients at high risk for BM, thereby enabling targeted MRI screenings and enhancing diagnostic efficiency.

Imaging in Lung Cancer Staging

Lung cancer remains one of the leading causes of mortality worldwide, as well as in the United States. Clinical staging, primarily with imaging, is integral to stratify patients into groups that determine treatment options and predict survival. The eighth edition of the tumor, node, metastasis (TNM-8) staging system proposed in 2016 by the International Association for the Study of Lung Cancer remains the current standard for lung cancer staging. The system is used for all subtypes of lung cancer, including non-small cell lung cancer, small cell lung cancer, and bronchopulmonary carcinoid tumors.

A Review of the Current Approach and Treatment Landscape for Stage III Non-Small Cell Lung Cancer

The therapeutic landscape of the management of stage III non-small cell lung cancer (NSCLC) has drastically evolved with the incorporation of immunotherapy and targeted therapy. Stage III NSCLC accounts for one-third of the cases and the treatment strategy of these locally advanced presentations are diverse, ranging from surgical to non-surgical options; with the incorporation of chemo-immunotherapy, radiation, and targeted therapies wherever applicable. The staging of this disease has also changed, and it is essential to have a strong multidisciplinary approach to do justice to patient care. In this article, we aim to navigate the nuanced approaches in the diagnosis and treatment of stage III NSCLC and expand on the evolution of the management of this disease.

Genomic Alterations and the Incidence of Brain Metastases in Advanced and Metastatic NSCLC: A Systematic Review and Meta-Analysis

INTRODUCTION

Brain metastases (BMs) in patients with advanced and metastatic NSCLC are linked to poor prognosis. Identifying genomic alterations associated with BM development could influence screening and determine targeted treatment. We aimed to establish prevalence and incidence in these groups, stratified by genomic alterations.

METHODS

A systematic review and meta-analysis compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses were conducted (PROSPERO identification CRD42022315915). Articles published in MEDLINE, EMBASE, and Cochrane Library between January 2000 and May 2022 were included. Prevalence at diagnosis and incidence of new BM per year were obtained, including patients with EGFR, ALK, KRAS, and other alterations. Pooled incidence rates were calculated using random effects models.

RESULTS

A total of 64 unique articles were included (24,784 patients with NSCLC with prevalence data from 45 studies and 9058 patients with NSCLC having incidence data from 40 studies). Pooled BM prevalence at diagnosis was 28.6% (45 studies, 95% confidence interval [CI]: 26.1-31.0), and highest in patients that are ALK-positive (34.9%) or with RET-translocations (32.2%). With a median follow-up of 24 months, the per-year incidence of new BM was 0.13 in the wild-type group (14 studies, 95% CI: 0.11-0.16). Incidence was 0.16 in the EGFR group (16 studies, 95% CI: 0.11-0.21), 0.17 in the ALK group (five studies, 95% CI: 0.10-0.27), 0.10 in the KRAS group (four studies, 95% CI: 0.06-0.17), 0.13 in the ROS1 group (three studies, 95% CI: 0.06-0.28), and 0.12 in the RET group (two studies, 95% CI: 0.08-0.17).

CONCLUSIONS

Comprehensive meta-analysis indicates a higher prevalence and incidence of BM in patients with certain targetable genomic alterations. This supports brain imaging at staging and follow-up, and the need for targeted therapies with brain penetrance.

Lung Cancer Staging: Imaging and Potential Pitfalls

Lung cancer is the leading cause of cancer deaths in men and women in the United States. Accurate staging is needed to determine prognosis and devise effective treatment plans. The International Association for the Study of Lung Cancer (IASLC) has made multiple revisions to the tumor, node, metastasis (TNM) staging system used by the Union for International Cancer Control and the American Joint Committee on Cancer to stage lung cancer. The eighth edition of this staging system includes modifications to the T classification with cut points of 1 cm increments in tumor size, grouping of lung cancers associated with partial or complete lung atelectasis or pneumonitis, grouping of tumors with involvement of a main bronchus regardless of distance from the carina, and upstaging of diaphragmatic invasion to T4. The N classification describes the spread to regional lymph nodes and no changes were proposed for TNM-8. In the M classification, metastatic disease is divided into intra- versus extrathoracic metastasis, and single versus multiple metastases. In order to optimize patient outcomes, it is important to understand the nuances of the TNM staging system, the strengths and weaknesses of various imaging modalities used in lung cancer staging, and potential pitfalls in image interpretation.

Imaging of Lung Cancer Staging

Lung cancer is a leading cause of cancer-related mortality worldwide. Imaging is integral in accurate clinical staging to stratify patients into groups to predict survival and determine treatment. The eighth edition of the tumor, node, and metastasis (TNM-8) staging system proposed by the International Association for the Study of Lung Cancer in 2016, accepted by both the Union for International Cancer Control and the American Joint Committee on Cancer, is the current standard method of staging lung cancer. This single TNM staging is used for all histologic subtypes of lung cancer, including nonsmall cell lung cancer, small cell lung cancer, and bronchopulmonary carcinoid tumor, and it addresses both clinical and pathologic staging. Familiarity with the strengths and limitations of imaging modalities used in staging, the nuances of TNM-8, its correct nomenclature, and potential pitfalls are important to optimize patient care. In this article, we discuss the role of computed tomography (CT) and positron emission tomography/CT in lung cancer staging, as well as current imaging recommendations pertaining to TNM-8.

BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients

The aim of the study was to analyze the use of block sequential regularized expectation maximization (BSREM) with different β-values for the detection of brain metastases in digital fluorine-18 labeled 2-deoxy-2-fluoro-D-glucose (18F-FDG) PET/CT in lung cancer patients. We retrospectively analyzed staging/restaging 18F-FDG PET/CT scans of 40 consecutive lung cancer patients with new brain metastases, confirmed by MRI. PET images were reconstructed using BSREM (β-values of 100, 200, 300, 400, 500, 600, 700) and OSEM. Two independent blinded readers (R1 and R2) evaluated each reconstruction using a 4-point scale for general image quality, noise, and lesion detectability. SUVmax of metastases, brain background, target-to-background ratio (TBR), and contrast recovery (CR) ratio were recorded for each reconstruction. Among all reconstruction techniques, differences in qualitative parameters were analyzed using non-parametric Friedman test, while differences in quantitative parameters were compared using analysis of variances for repeated measures. Cohen's kappa (k) was used to measure inter-reader agreement. The overall detectability of brain metastases was highest for BSREM200 (R1: 2.83 ± 1.17; R2: 2.68 ± 1.32) and BSREM300 (R1: 2.78 ± 1.23; R2: 2.68 ± 1.36), followed by BSREM100, which had lower accuracy owing to noise. The highest median TBR was found for BSREM100 (R1: 2.19 ± 1.05; R2: 2.42 ± 1.08), followed by BSREM200 and BSREM300. Image quality ratings were significantly different among reconstructions (p < 0.001). The median quality score was higher for BSREM100-300, and both noise and metastases' SUVmax decreased with increasing β-value. Inter-reader agreement was particularly high for the detectability of photopenic metastases and blurring (all k > 0.65). BSREM200 and BSREM300 yielded the best results for the detection of brain metastases, surpassing both BSREM400 and OSEM, typically used in clinical practice.

Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer

Background

This study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.

Methods

A total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.

Results

For distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.

Conclusions

Our study developed a “two-step” ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system.

Management of Stage III Non-Small-Cell Lung Cancer: ASCO Guideline

PURPOSE

To provide evidence-based recommendations to practicing clinicians on management of patients with stage III non-small-cell lung cancer (NSCLC).

METHODS

An Expert Panel of medical oncology, thoracic surgery, radiation oncology, pulmonary oncology, community oncology, research methodology, and advocacy experts was convened to conduct a literature search, which included systematic reviews, meta-analyses, and randomized controlled trials published from 1990 through 2021. Outcomes of interest included survival, disease-free or recurrence-free survival, and quality of life. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations.

RESULTS

The literature search identified 127 relevant studies to inform the evidence base for this guideline.

RECOMMENDATIONS

Evidence-based recommendations were developed to address evaluation and staging workup of patients with suspected stage III NSCLC, surgical management, neoadjuvant and adjuvant approaches, and management of patients with unresectable stage III NSCLC.Additional information is available at www.asco.org/thoracic-cancer-guidelines.

Overuse of Diagnostic Brain Imaging Among Patients With Stage IA Non-Small Cell Lung Cancer

BACKGROUND

Among patients diagnosed with stage IA non-small cell lung cancer (NSCLC), the incidence of occult brain metastasis is low, and several professional societies recommend against brain imaging for staging purposes. The goal of this study was to characterize the use of brain imaging among Medicare patients diagnosed with stage IA NSCLC.

METHODS

Using data from linked SEER-Medicare claims, we identified patients diagnosed with AJCC 8th edition stage IA NSCLC in 2004 through 2013. Patients were classified as having received brain imaging if they underwent head CT or brain MRI from 1 month before to 3 months after diagnosis. We identified factors associated with receipt of brain imaging using multivariable logistic regression.

RESULTS

Among 13,809 patients with stage IA NSCLC, 3,417 (25%) underwent brain imaging at time of diagnosis. The rate of brain imaging increased over time, from 23.5% in 2004 to 28.7% in 2013 (P=.0006). There was significant variation in the use of brain imaging across hospital service areas, with rates ranging from 0% to 64.0%. Factors associated with a greater likelihood of brain imaging included older age (odds ratios [ORs] of 1.16 for 70-74 years, 1.13 for 75-79 years, 1.31 for 80-84 years, and 1.46 for ≥85 years compared with 65-69 years; all P<.05), female sex (OR, 1.09; P<.05), black race (OR 1.23; P<.05), larger tumor size (ORs of 1.23 for 11-20 mm and 1.28 for 21-30 mm tumors vs 1-10 mm tumors; all P<.05), and higher modified Charlson-Deyo comorbidity score (OR, 1.28 for score >1 vs score of 0; P<.05).

CONCLUSIONS

Roughly 1 in 4 patients with stage IA NSCLC received brain imaging at the time of diagnosis despite national recommendations against the practice. Although several patient factors are associated with receipt of brain imaging, there is significant geographic variation across the United States. Closer adherence to clinical guidelines is likely to result in more cost-effective care.

The effect of diagnostic assessment programs on the diagnosis and treatment of patients with lung cancer in Ontario, Canada

INTRODUCTION

Diagnostic assessment programs (DAPs) were implemented in Ontario, Canada, to improve the efficiency of the lung cancer care continuum. We compared the efficiency and effectiveness of care provided to patients in DAPs relative to usual care (non-DAPs).

METHODS

Lung cancer patients diagnosed between 2014 and 2016 were identified from the Ontario Cancer Registry. Using administrative databases, we identified various health-care encounters 6 months before diagnosis until the start of treatment and compared utilization patterns, timing, and overall survival between DAP and non-DAP patients.

RESULTS

DAP patients were younger (P < 0.0001), had fewer comorbidities (P = 0.0006), and were more likely to have early-stage disease (36% vs. 25%) than non-DAP patients. Although DAP patients had a similar time until diagnosis as non-DAP patients, the time until treatment was 8.5 days shorter for DAP patients. DAP patients were more likely to receive diagnostic tests and specialist consultations and less likely to have duplicate chest imaging. DAP patients were more likely to receive brain imaging. Among early-stage lung cancers, brain imaging was high (74% for DAP and 67% for non-DAP), exceeding guideline recommendations. After adjustment for clinical and demographic factors, DAP patients had better overall survival than non-DAP patients (hazard ratio [HR]: 0.79 [0.76-0.82]), but this benefit was lost after adjusting for emergency presentation (HR: 0.96 [0.92-1.00]). A longer time until treatment was associated with better overall survival.

CONCLUSION

DAPs provided earlier treatment and better access to care, potentially improving survival. Quality improvement opportunities include reducing unnecessary or duplicate testing and characterizing patients who are diagnosed emergently.

Incidence and real-world burden of brain metastases from solid tumors and hematologic malignancies in Ontario: a population-based study

BACKGROUND

Although intracranial metastatic disease (IMD) is a frequent complication of cancer, most cancer registries do not capture these cases. Consequently, a data-gap exists, which thwarts system-level quality improvement efforts. The purpose of this investigation was to determine the real-world burden of IMD.

METHODS

Patients diagnosed with a non-CNS cancer between 2010 and 2018 were identified from the Ontario Cancer Registry. IMD was identified by scanning hospital administrative databases for cranial irradiation or coding for a secondary brain malignancy (ICD-10 code C793).

RESULTS

25,478 of 601,678 (4.2%) patients with a diagnosis of primary cancer were found to have IMD. The median time from primary cancer diagnosis to IMD was 5.2 (0.7, 15.4) months and varied across disease sites, for example, 2.1 months for lung, 7.3 months for kidney, and 22.8 months for breast. Median survival following diagnosis with IMD was 3.7 months. Lung cancer accounted for 60% of all brain metastases, followed by breast cancer (11%) and melanoma (6%). More advanced stage at diagnosis and younger age were associated with a higher likelihood of developing IMD (P < .0001). IMD was also associated with triple-negative breast cancers and ductal histology (P < .001), and with small-cell histology in patients with lung cancer (P < .0001). The annual incidence of IMD was 3,520, translating to 24.2 per 100,000 persons.

CONCLUSION

IMD represents a significant burden in patients with systemic cancers and is a significant cause of cancer mortality. Our findings support measures to actively capture incidents of brain metastasis in cancer registries.

Long-term Survival with 18-Fluorodeoxyglucose Positron Emission Tomography-directed Therapy in Non-small Cell Lung Cancer with Synchronous Solitary Brain Metastasis

AIMS

At diagnosis, <1% of patients with non-small cell lung cancer (NSCLC) have synchronous solitary brain metastasis (SSBM). In prior cohorts without 18-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) staging, definitive treatment to intracranial and intrathoracic disease showed a 5-year overall survival (OS) of 11-21%. We investigated the long-term survival outcomes for patients with SSBM NSCLC, diagnosed in the FDG-PET/CT era and treated definitively with local therapies to both intracranial and intrathoracic sites of disease.

MATERIALS AND METHODS

This retrospective study assessed patients staged with FDG-PET/CT who received definitive lung and SSBM treatment from February 1999 to December 2017. A lung-molecular graded prognostic assessment (lung-molGPA) score was assigned for each patient using age, performance status score, and, where carried out, molecular status. Overall survival and progression-free survival (PFS) were calculated using Kaplan-Meier methods. Cox proportional hazard models determined OS and PFS prognostic factors.

RESULTS

Forty-nine patients newly diagnosed with NSCLC and SSBM had a median age of 63 years (range 34-76). The median follow-up of all patients was 3.9 years. Thirty-three patients (67%) had ≥T2 disease, 23 (47%) had ≥N2. At 2 years, 45% of first failures were intracranial only (95% confidence interval 30-59). At 3 and 5 years, OS was 45% (95% confidence interval 32-63) and 30% (95% confidence interval 18-51), respectively. In ≥N1 disease, 5-year OS was 34% (95% confidence interval 18-63). The 3- and 5-year PFS was 8% (95% confidence interval 3-22) and 0%, respectively. Higher lung-molGPA was associated with longer OS (hazard ratio 0.26, 95% confidence interval 0.11-0.61, P = 0.002). Higher lung-molGPA (hazard ratio 0.33, 95% confidence interval 0.15-0.71, P = 0.005) and lower N-stage (hazard ratio 1.56, 95% confidence interval 1.13-2.15, P = 0.007) were associated with longer PFS.

CONCLUSIONS

Definitive treatment of patients with NSCLC and SSBM staged with FDG-PET/CT can result in 5-year survivors, including those with ≥N1 disease.

PET imaging in patients with brain metastasis-report of the RANO/PET group

Brain metastases (BM) from extracranial cancer are associated with significant morbidity and mortality. Effective local treatment options are stereotactic radiotherapy, including radiosurgery or fractionated external beam radiotherapy, and surgical resection. The use of systemic treatment for intracranial disease control also is improving. BM diagnosis, treatment planning, and follow-up is most often based on contrast-enhanced magnetic resonance imaging (MRI). However, anatomic imaging modalities including standard MRI have limitations in accurately characterizing posttherapeutic reactive changes and treatment response. Molecular imaging techniques such as positron emission tomography (PET) characterize specific metabolic and cellular features of metastases, potentially providing clinically relevant information supplementing anatomic MRI. Here, the Response Assessment in Neuro-Oncology working group provides recommendations for the use of PET imaging in the clinical management of patients with BM based on evidence from studies validated by histology and/or clinical outcome.

Epidemiologic and histologic characteristics of CNS lesions: a 20-year experience of a tertiary center in Lebanon

Aim:

Report the epidemiologic and histologic characteristics of CNS lesions in the Lebanese population.

Methods:

We conducted a retrospective study evaluating 2025 CNS lesions diagnosed between 1998 and 2017 in the pathology laboratory of a Lebanese tertiary center.

Results:

52.2% of patients were men with a median age of 50 years. The most frequent symptoms were epilepsy (22.5%), headache (20.6%) and motor impairment (19.9%). 90.7% of tumors were primary. Lung (35.6%) and breast (16.5%) were the most frequent primaries of metastases. 46.2% of primary CNS tumors were glial, predominantly astrocytic (56.4%), and (42.5%) were nonglial, predominantly meningeal tumors (58%).

Conclusion:

Compared with Western literature, the Lebanese population is characterized by a younger age of onset of brain tumors, a lower rate of meningiomas and a higher rate of gliomas.

Risk of brain metastases in T1-3N0 NSCLC: a population-based analysis

Aim:

Several consensus guidelines recommend against routine brain imaging at diagnosis of T1-3N0 non-small cell lung cancer (NSCLC).

Methods:

From the Surveillance, Epidemiology and End Results registry, patients with pathologically confirmed T1-3N0 NSCLC were identified. Risks of brain metastases at time of initial diagnosis were analyzed.

Results:

Patients selected to not undergo primary NSCLC resection had approximately tenfold greater incidence of brain metastases versus those who did. Younger age, adenocarcinoma histology, higher tumor stage and higher histologic grade were all significantly (p < 0.0001) associated with greater likelihood of presenting with brain metastases.

Conclusion:

Given the morbidity and mortality of brain metastases, routine brain screening after NSCLC diagnosis (particularly adenocarcinoma) may be justifiable, though more refined cost-benefit analyses are warranted.

CT-Based Radiomics Model for Predicting Brain Metastasis in Category T1 Lung Adenocarcinoma

OBJECTIVE. The purpose of this study is to develop and evaluate an unenhanced CT-based radiomics model to predict brain metastasis (BM) in patients with category T1 lung adenocarcinoma. MATERIALS AND METHODS. A total of 89 eligible patients with category T1 lung adenocarcinoma were enrolled and classified as patients with BM (n = 35) or patients without BM (n = 54). A total of 1160 quantitative radiomic features were extracted from unenhanced CT images of each patient. Three prediction models (the clinical model, the radiomics model, and a hybrid [clinical plus radiomics] model) were established. The ROC AUC value and 10-fold cross-validation were used to evaluate the prediction performance of the models. RESULTS. In terms of predictive performance, the mean AUC value was 0.759 (95% CI, 0.643-0.867; sensitivity, 82.9%; specificity, 57.4%) for the clinical model, 0.847 (95% CI, 0.739-0.915; sensitivity, 80.0%; specificity, 81.5%) for the radiomics model, and 0.871 (95% CI, 0.767-0.933; sensitivity = 82.9%, specificity = 83.3%) for the hybrid model. The hybrid and radiomics models (p = 0.0072 and 0.0492, respectively) performed significantly better than the clinical model. No significant difference was found between the radiomics model and the hybrid model (p = 0.1022). CONCLUSION. A CT-based radiomics model presented good predictive performance and great potential for predicting BM in patients with category T1 lung adenocarcinoma. As a promising adjuvant tool, it can be helpful for guiding BM screening and thus benefiting personalized surveillance for patients with lung cancer.

Patterns of care for stage III non-small cell lung cancer in Australia

Stage III non-small cell lung cancer (NSCLC) makes up a third of all NSCLC cases and is potentially curable. Despite this 5-year survival rates remain between 15% and 20% with chemoradiation treatment alone given with curative intent. With the recent exciting breakthroughs in immunotherapy use (durvalumab) for stage III NSCLC, further improvements in patient survival can be expected. Most patients with stage III NSCLC present initially to their general practitioner (GP). The recommended time from GP referral to first specialist appointment is less than 14 days with treatment initiated within 42 days. Our review found that there is a shortfall in meeting these recommendations, however a number of initiatives have been established in Australia to improve timely and accurate diagnosis and treatment patterns. The lung cancer multidisciplinary team (MDT) is critical to consistency of evidence-based diagnosis and treatment and can improve patient survival. We aimed to review current patterns of care and clinical practice recommendations for stage III NSCLC across Australia and identify opportunities to improve practice in referral, diagnosis and treatment pathways.

Prognostic impact of combining whole-body PET/CT and brain PET/MR in patients with lung adenocarcinoma and brain metastases

PURPOSE

The role of brain FDG-PET in patients with lung cancer and brain metastases remains unclear. Here, we sought to determine the prognostic significance of whole-body PET/CT plus brain PET/MR in predicting the time to neurological progression (nTTP) and overall survival (OS) in this patient group.

METHODS

Of 802 patients with non-small cell lung cancer who underwent primary staging by a single-day protocol of whole-body PET/CT plus brain PET/MR, 72 cases with adenocarcinoma and brain metastases were enrolled for a prognostic analysis of OS. On the basis of the available follow-up brain status, only 52 patients were eligible for prognostic analysis of nTTP. Metastatic brain tumors were identified on post-contrast MR imaging, and the tumor-to-brain ratio (TBR) was measured on PET images.

RESULTS

Multivariate analysis revealed that FDG-PET findings and eligibility for initial treatment with targeted therapy were significant independent predictors of nTTP and OS. A new index, termed the molecular imaging prognostic (MIP) score, was proposed to define three disease classes. MIP scores were significant predictors of both nTTP and OS (P < 0.001). Pre-existing prognostic indices such as Lung-molGPA scores were significant predictors of OS but did not predict nTTP.

CONCLUSIONS

When staging is performed with whole-body PET/CT plus brain PET/MR, our new prognostic index may be helpful to stratify the outcomes of patients with lung adenocarcinoma and brain metastases. The superior prognostic power of this index for nTTP might be used to select appropriate patients for intracranial control and thereby achieve better quality of life.