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1.
J Thorac Dis ; 12(10): 5324-5335, 2020 Oct.
Article in English | MEDLINE | ID: mdl-33209366

ABSTRACT

BACKGROUND: Although neoadjuvant chemotherapy could improve survival outcome in resectable non-small cell lung cancer (NSCLC), the efficacy of neoadjuvant targeted therapy is still unclear. METHODS: We retrospectively reviewed clinical records of stage I-IIIA lung adenocarcinoma patients treated with neoadjuvant targeted therapy or chemotherapy prior to surgery. The collected data were compared between the two groups. Tumor samples were collected and analyzed by sequencing to explore the epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) resistance mechanisms. RESULTS: A total of 134 patients were enrolled; of these, 119 (88.8%) had clinical stage II-IIIA disease. Radiographic response rate was significantly higher with neoadjuvant targeted therapy than with chemotherapy among patients harboring EGFR mutation [objective response rate (ORR): 55.8% vs. 32.6%; P=0.030]. EGFR exon 19 deletion achieved better tumor response than those with exon 21 L858R mutation (ORR: 70.0% vs. 40.0%; P=0.057). Postoperative complications, operation time, drainage volume, and postoperative hospital length of stay were comparable between two groups. There was no difference on disease free survival (DFS) between patients receiving neoadjuvant targeted therapy and chemotherapy (P=0.871), but those who continued long-term adjuvant targeted therapy had significantly longer DFS than those only treated with adjuvant chemotherapy postoperatively (P=0.011). A series of potential molecular mechanisms of EGFR-TKI primary resistance were detected; these included BIM deletion polymorphisms, EGFR T790M mutation, and PTEN, TSC1, PIK3CA, or STAT3 mutations. Patients who presented stable disease (SD) response after TKI therapy had significantly lower EGFR mutation abundance than PR response (P=0.032). CONCLUSIONS: Neoadjuvant EGFR-TKI appears to be more effective than conventional chemotherapy for EGFR-mutant NSCLC patients. This study provides evidence that needs to be investigated further in randomized controlled trials (RCT).

2.
Oncotarget ; 8(37): 61662-61673, 2017 Sep 22.
Article in English | MEDLINE | ID: mdl-28977894

ABSTRACT

The prognosis of patients with esophageal cancer improves by using neoadjuvant chemotherapy (NAC). More patients obtain pathological N0 staging (pN0) after surgery. The heterogeneity of prognosis of these patients poses a great challenge of customizing therapeutic strategies for individual patients. The signs of lymph nodes on both pre and post NAC computer tomography (CT) scan can provide more information for evaluation. Therefore, we investigated a new approach to lymph node (LN)-survival analysis by using pre-/post-NAC CT in pN0 esophageal cancer. 79 patients undergone curative resection after NAC obtained pN0 staging. The long and short axis diameter of maximal lymph node (MaxLN) and LN number on pre-/post-NAC CT scans were recorded and assessed for predicting survival by univariate and multivariate survival analysis. The prognosis of patients with esophageal cancer was correlated with the LN size and number on pre-/post-NAC CT. The LN number on pre-NAC CT and short-axis diameter of MaxLN on post-NAC CT remained the independent predictor of overall survival. By using these two factors as classification criterion, N0b group included patients with LN number>4 on pre-NAC CT or short-axis diameter of MaxLN >7 mm on post-NAC CT and the rest patients were included in N0a group. N0a group had a significantly better overall survival than N0b group (5-year survival rate: 75.2% vs. 32.6%). The size and number of lymph node on pre-/post-NAC CT were reliable and important prognostic factors in patients with pN0 esophageal cancer. This new criterion could distinguish these patients into N0a and N0b, according to different prognosis.

3.
Eur J Radiol ; 77(3): 473-7, 2011 Mar.
Article in English | MEDLINE | ID: mdl-19853396

ABSTRACT

PURPOSE: To assess the extent to which pretreatment imaging of lymph nodes by computed tomography (CT) predicts survival of patients with rectal cancer. MATERIALS AND METHODS: We retrospectively analyzed 70 patients with rectal cancer, who had pretreatment CT and curative surgery between December 1999 and October 2003. These patients were followed until December 2007, ensuring minimal follow-up time of 49 months. Two radiologists who reviewed the CT images on workstations had no prior access to clinical and treatment information regarding the selected patients. The parameters assessed for survival analysis were as follows: patient age, sex, CEA and CA199 level, preoperational therapy, tumor location, serosal invasion, largest diameters and numbers of lymph nodes on pretreatment CT. Kaplan-Meier survival curves, the log-rank test, and the multivariate Cox proportional hazards model were used to evaluate the prognostic value of the parameters. RESULTS: Using pretreatment CT as prognostic tool, we found that both size and number of lymph nodes correlated with the overall survival of patients with rectal cancer. The data proved that a diameter smaller than 8mm for the largest lymph node was correlated with prolonged survival (P < 0.001). Meanwhile, patients with more than 4 lymph nodes had a significantly worse disease-specific survival (P = 0.042). Both parameters are independent prognostic factors (hazard ratio 4.910 and 3.563) and could predict the overall survival of rectal cancer patients. CONCLUSION: The lymph node size and number, as determined by pretreatment CT, is an important clinical prognostic factor in patients with rectal cancer. The pretreatment CT findings could be used to predict survival and plan appropriate therapies.


Subject(s)
Lymph Nodes/diagnostic imaging , Rectal Neoplasms/diagnostic imaging , Rectal Neoplasms/mortality , Tomography, X-Ray Computed/statistics & numerical data , Adult , Aged , Aged, 80 and over , China/epidemiology , Female , Humans , Lymphatic Metastasis , Male , Middle Aged , Prevalence , Rectal Neoplasms/surgery , Risk Assessment , Risk Factors , Survival Analysis , Survival Rate
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