Prognostic value of surgical intervention in advanced lung adenocarcinoma: a population-based study

Single-molecule targeted therapy shrinks lung lesions and improves bone metastases: A case report

RATIONALE

Bone metastasis is a common metastatic mode of advanced lung cancer and poses a great threat to the survival and quality of life of patients with this disease. However, the available literature has limited treatment options for advanced lung cancer with bone metastases.

PATIENTS CONCERNS

A 76-year-old married male patient was underwent CT due to cough and sputum for 1 month. On CT, space-occupying lesions were found in the left inferior lobe of the lung, as well as multiple bone metastases in the vertebral body and ilium.

DIAGNOSES

Pathologic sectioning of the lung lesion after puncture revealed invasive lung adenocarcinoma, and a genetic test revealed EGFR exon 21: L858R (64.60%).

INTERVENTIONS

Considering that the disease was not suitable for radiotherapy (extensive metastasis) and could not be treated with chemotherapy (poor underlying condition), the patient was given molecularly targeted therapy with osimertinib.

OUTCOMES

After 10 months of standard treatment (80 mg orally, once a day), the lung lesions of the patients became significantly smaller, and the bone metastases distinctly improved. And the patient's condition has not shown any signs of rebound with the one-year follow-up.

LESSONS SUBSECTIONS

In the present case, the bone metastases from lung adenocarcinoma almost completely disappeared after treatment with a single molecular targeted therapy agent, increasing the confidence in the treatment of advanced lung cancer.

Distinguishing optimal candidates for primary tumor resection in patients with metastatic lung adenocarcinoma: A predictive model based on propensity score matching

Background

Primary tumor resection is associated with survival benefits in patients with metastatic lung adenocarcinoma (mLUAD). However, there are no established methods to determine which individuals would benefit from surgery. Therefore, we developed a model to predict the patients who are likely to benefit from surgery in terms of survival.

Methods

Data on patients with mLUAD were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Depending on whether surgery was performed on the primary tumor, patients were categorized into two groups: cancer-directed surgery (CDS) and no-cancer-directed surgery (No-CDS). Propensity Score Matching (PSM) was utilized to address bias between the CDS and No-CDS groups. The prognostic impact of CDS was assessed using Kaplan-Meier analysis and Cox proportional hazard models. Subsequently, we constructed a nomogram to predict the potential for surgical benefits based on multivariable logistic regression analysis using preoperative factors.

Results

A total of 89,039 eligible patients were identified, including 6.4% (5705) who underwent surgery. Following PSM, the CDS group demonstrated a significantly longer median overall survival (mOS) compared with the No-CDS group (23 [21-25] vs. 7 [7-8] months; P < 0.001). The nomogram showed robust performance in both the training and validation sets (area under the curve [AUC]: 0.698 and 0.717, respectively), and the calibration curves exhibited high consistency. The nomogram proved clinically valuable according to decision curve analysis (DCA). According to this nomogram, surgical patients were categorized into two groups: no-benefit candidates and benefit candidates groups. Compared with the no-benefit candidate group, the benefit candidate group was associated with longer survival (mOS: 25 vs. 6 months, P < 0.001). Furthermore, no difference in survival was observed between the no-benefit candidates and the no-surgery groups (mOS: 6 vs. 7 months, P = 0.9).

Conclusions

A practical nomogram was developed to identify optimal CDS candidates among patients with mLUAD.

Correlation analysis of EGFR gene mutation abundance and the efficacy of targeted therapy with osimertinib in nonsmall cell lung cancer-a case control study

BACKGROUND

In nonsmall cell lung cancer (NSCLC), epidermal growth factor receptor (EGFR) mutation is the primary cancer-causing mutation. But whether the practical effectiveness of EGFR tyrosine kinase inhibitors (TKIs) can be influenced by plasma EGFR mutation abundance when treating patients with advanced NSCLC remains unanswered. Therefore, this research was intended to reveal the connection between plasma EGFR mutation abundance and clinical outcomes in osimertinib-treated patients with advanced NSCLC.

METHODS

A total of 120 patients with advanced NSCLC were retrospectively analyzed, and 56 patients with EGFR-mutation-positive NSCLC receiving osimertinib first-line therapy were eventually screened and included. The baseline status and abundance of plasma EGFR in patients with NSCLC were detected by cSMART, and the ratio of 0.1 was the critical value. Imaging examinations were performed every 8-12 weeks for the assessment of tumor response. The relationship between baseline EGFR mutation abundance and clinical outcomes of TKI therapy was analyzed.

RESULTS

The objective response rates (ORR) of EGFR-mutant patients in the high-/low-abundance groups were 69.2% and 40.0%, respectively. The high abundance group had an obviously higher ORR than the low abundance group (P = 0.029). A much longer median progression-free survival (mPFS) was demonstrated in patients with high mutation abundance than in patients with low abundance (11.2 months vs 7.1 months, P = 0.0133). As for the median overall survival (mOS), it showed the same trend as mPFS in patients from different groups (15.5 vs 10.7 months, P = 0.0028). The role of plasma mutation abundance as an independent prognostic factor for both PFS (hazard ratios [HR]: 0.30, P = 0.006) and OS (HR: 0.35, P = 0.004) was demonstrated by multivariate Cox regression analysis.

CONCLUSION

There is a close connection between plasma EGFR mutation abundance and survival benefit in patients with NSCLC, which can be used for predicting the efficacy of EGFR-TKI targeted therapy. Our study is expected to provide a research basis for screening patients to whom the EGFR-TKI therapy is beneficial.

Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

BACKGROUND

Nomogram is a graphic representation containing the expressed factor of the mathematical formula used to define a particular phenomenon. We aim to build and internally validate a nomogram to predict overall survival (OS) in patients diagnosed with lung cancer (LC).

METHODS

We included 1200 LC patients from a single institution registry diagnosed from 2013 to 2021. The independent prognostic factors of LC patients were identified via cox proportional hazard regression analysis. Based on the results of multivariate cox analysis, we constructed the nomogram to predict the OS of LC patients.

RESULTS

We finally included a total of 1104 LC patients. Age, medical urgency at diagnosis, performance status, radiotherapy, and surgery were identified as prognostic factors, and integrated to build the nomogram. The model performance in predicting prognosis was measured by receiver operating characteristic curve. Calibration plots of 6-, 12-, and 24- months OS showed optimal agreement between observations and model predictions.

CONCLUSION

We have developed and validated a unique predictive tool that can offer patients with LC an individual OS prognosis. This useful prognostic model could aid doctors in making decisions and planning therapeutic trials.

Stage IV non-small cell lung cancer among young individuals: Incidence, presentations, and survival outcomes of conventional therapies

Background

There is a paucity of data published on the clinicopathological features and prognosis of stage IV non-small cell lung cancer (NSCLC) patients aged ≤45 years. Herein, we evaluated a large clinical series in an effort to provide a clearer picture of this population.

Methods

The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression model was performed to identify prognostic factors for NSCLC among individuals aged ≤45 years. The Kaplan–Meier method with log-rank test was used to compare overall survival (OS) differences between groups. Competing risk analysis with the Fine–Gray test was used to analyze cancer-specific survival (CSS) differences. Propensity score matching (PSM) was used to minimize selection bias.

Results

Incidence-rate analyses, including 588,680 NSCLC cases (stage IV, 233,881; age ≤ 45 years stage IV, 5,483; and age > 45 years stage IV, 228,398) from 2004 to 2015, showed that the incidence of stage IV NSCLC among young individuals decreased over the years. In comparative analyses of clinical features and survival outcomes, a total of 48,607 eligible stage IV cases (age ≤ 45 years stage IV, 1,390; age > 45 years stage IV, 47,217) were included. The results showed that although patients in the young cohort were more likely to be diagnosed at advanced stages, they were also more likely to receive aggressive treatments. In addition, the survival rates of the young patients were superior to those of the older patients both before and after PSM.

Conclusions

Stage IV NSCLC patients aged ≤45 years comprise a relatively small but special NSCLC subgroup. Although this population had better survival outcomes than older patients, these patients deserve more attention due to their young age and the significant socioeconomic implications.

The nomogram for the prediction of overall survival in patients with metastatic lung adenocarcinoma undergoing primary site surgery: A retrospective population-based study

Background

Lung adenocarcinoma (LUAD) is the most common type of Non-small-cell lung cancer (NSCLC). Distant metastasis of lung adenocarcinoma reduces the survival rate. we aim to develop a nomogram in order to predict the survival of patients with metastatic lung adenocarcinoma.

Methods

We retrospectively collected patients who were initially diagnosed as metastatic LUAD from 2010 to 2015 from SEER database. Based on the multivariate and univariate Cox regression analysis of the training cohorts, independent prognostic factors were assessed. The nomogram prediction model was then constructed based on these prognostic factors to predict the overall survival at 12, 24 and 36 months after surgery. Nomogram were identified and calibrated by c-index, time-dependent receiver operating characteristic curve (time-dependent AUC) and calibration curve. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities, and to better compare with the TNM staging system, we calculated the c-index of this nomogram as well as the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI).

Result

A total of 1102 patients with metastatic LUAD who met the requirements were included for analysis. They were randomly divided into 774 in the training cohorts and 328 in the validation cohorts. As can be seen from the calibration plots, the predicted nomogram and the actual observations in both of the training and validation cohorts were generally consistent. The time dependent AUC values of 12 months, 24 months and 36 months were 0.707, 0.674 and 0.686 in the training cohorts and 0.690, 0.680 and 0.688 in the verification cohorts, respectively. C-indexes for the training and validation cohorts were 0.653 (95%CI 0.626-0.68)and 0.663 (95%CI 0.626-1), respectively. NRI and IDI show that the model is more clinical applicable than the existing staging system. In addition, our risk scoring system based on Kaplan Meier (K-M) survival curve can accurately divide patients into three hierarchy risk groups.

Conclusion

This has led to the development and validation of a prognostic nomogram to assist clinicians in determining the prognosis of patients with metastatic lung adenocarcinoma after primary site surgery.

Impact of albumin infusion on prognosis of intensive care unit patients with congestive heart failure-hypoalbuminemia overlap: a retrospective cohort study

Background

Hypoalbuminemia is common in congestive heart failure (CHF) patients. Serum albumin is associated with the prognosis of CHF patients. Impact of albumin infusion on prognosis of patients with CHF-hypoalbuminemia overlap remains unclear. We retrospectively investigated the impact of albumin infusion on prognosis of intensive care unit (ICU) patients with CHF-hypoalbuminemia overlap.

Methods

We enrolled all patients whose diagnosis included CHF [ICD-9 (international classification of diseases 9) code =428.0] at first ICU admission from the MIMIC III (Medical Information Mart for Intensive Care III) database, and excluded those with missing serum albumin values, with serum albumin >3.4 g/dL or <18 years old. According to the exposure of albumin infusion during hospitalization, patients were stratified into non-albumin and albumin groups. Propensity-score matching (PSM) was performed (1:1 ratio) to control for baseline confounding. Outcome measures were in-hospital mortality as well as length of stay in the ICU (ICU LOS) and the hospital (hospital LOS).

Results

There were 3,190 eligible patients in the initial search. Patients with albumin infusion had markedly higher in-hospital mortality (36.42% vs. 21.81%, P<0.001), longer ICU LOS [median 6.93 (3.39–14.82) vs. 3.84 (1.96–8.00) days, P<0.001], and longer hospital LOS [median 17.46 (11.45–28.33) vs. 10.92 (6.81–18.00) days, P<0.001] than those without albumin infusion. The multivariate logistic regression analysis revealed that albumin infusion [odds ratio (OR), 1.509; 95% confidence interval (CI), 1.164–1.957; P=0.002] was significantly associated with increased risk of in-hospital mortality. After PSM, a cohort of 429 pairs of patients was included in the final analysis. Patients with albumin infusion had markedly higher in-hospital mortality (34.97% vs. 27.27%, P=0.015), longer ICU LOS [median 8.43 (4.33–16.28) vs. 6.43 (3.07–13.66) days, P<0.001], and longer hospital LOS [median 16.92 (11.27–28.06) vs. 13.33 (8.00–21.10) days, P<0.001] than those without albumin infusion. The multivariate logistic regression analysis revealed that albumin infusion (OR, 1.594; 95% CI, 1.143–2.223; P=0.006) was significantly associated with increased risk of in-hospital mortality.

Conclusions

Albumin infusion increased in-hospital mortality, ICU LOS, and hospital LOS in ICU patients with CHF-hypoalbuminemia overlap.