The feasibility of surgery after immunotherapy for mediastinal liposarcoma remains uncertain. Besides, the case of immunotherapy for liposarcoma is still lacking. We report a case of recurrence after resection of a left mediastinal liposarcoma. After recurrence, one course of pembrolizumab plus anlotinib hydrochloride showed no tumor shrinkage, and genetic testing showed CDK4 amplification and PD-L1 TPS <1%; therefore, the plan was changed to one course of pembrolizumab plus palbociclib, but the tumor still did not shrink. Thus, second tumor resection was performed. In addition, the postoperative pathology was still well-differentiated liposarcoma. The significance of immunotherapy in liposarcoma still needs to be further explored. In the absence of surgical contraindications, secondary surgery might be feasible.
Background: The survival of patients with stage IA-IIA non-small cell lung cancer (NSCLC) after surgery is heterogeneous. This study aimed to construct a prognostic risk model to predict the overall survival (OS) of these patients. Methods: Data from patients (n=9,914) from the Surveillance Epidemiology and End Results (SEER) database were analyzed. The cases were randomly divided into the training and the validation groups. Patients from the Shanghai Pulmonary Hospital (n=270) were also included as an external cohort. Independent significant factors affecting survival in the training cohort were used to construct a nomogram. The precision was evaluated using the concordance index (C-index) and calibration plots. The X-tile software was used to confirm the optimal cut-off value to classify the patients. Results: Sex, age at diagnosis, tumor size, visceral pleura invasion (VPI), tumor grade, and the number of examined lymph nodes were deemed independent prognostic factors and were selected to establish the nomogram. The C-indices of the nomogram for predicting OS were 0.671 [95% confidence interval (CI): 0.653-0.689] in the training group, and 0.668 (95% CI: 0.650-0.687) and 0.707 (95% CI: 0.651-0.763) in the validation and the testing groups, respectively. The cut-off value of risk points was 106.0, which stratified the patients into high-risk and low-risk groups. The high-risk patients had shorter 5-year OS than low-risk patients (P<0.001). Conclusions: The established nomogram could evaluate the survival in patients with stage IA-IIA NSCLC after surgery and may provide prognostic information for clinicians to make decisions in the management of adjuvant therapy.
Objective: The prognostic effect of delayed treatment on stage IA1 non-small cell lung cancer (NSCLC) patients is still unclear. This study aimed to explore the association between the waiting time before treatment and the prognosis in stage IA1 NSCLC patients. Methods: Eligible patients diagnosed with pathological stage IA1 NSCLC were included in this study. The clinical endpoints were overall survival (OS) and cancer-specific survival (CSS). The Kaplan-Meier method, the Log-rank test, univariable, and multivariable Cox regression analyses were used in this study. Propensity score matching was used to reduce the bias of data distribution. Results: There were eligible 957 patients in the study. The length of waiting time before treatment stratified the survival in patients [<3 months vs. ≥3-months, unadjusted hazard ratio (HR) = 0.481, P = 0.007; <2 months vs. ≥2-months, unadjusted HR = 0.564, P = 0.006; <1 month vs. ≥1-month, unadjusted HR = 0.537, P = 0.001]. The 5-year CSS rates were 95.0% and 77.0% in patients of waiting time within 3 months and over 3 months, respectively. After adjusting for other confounders, the waiting time was identified as an independent prognostic factor. Conclusions: A long waiting time before treatment may decrease the survival of stage IA1 NSCLC patients. We propose that the waiting time for those patients preferably is less than one month and should not exceed two months.
This study constructed and validated a prognostic model to evaluate the survival of small-cell lung cancer (SCLC) patients following surgery, and shed light on the strategy of postoperative radiotherapy. A total of 882 patients from Shanghai Pulmonary Hospital and the Surveillance, Epidemiology and End Results database after lung resection were selected. Multivariable Cox analysis was used to identify the indicators affecting long-term survival in patients. A nomogram was constructed to predict the prognosis of eligible patients. Indices of concordance (C-index) was used to access the predictive ability of cancer-specific survival (CSS) for the prognostic model. CSS discrimination in the prognostic model was comparable in the training and validation cohorts (C-index = 0.637[NORAD-T], 0.660[NORAD-V], 0.656[RAD] and 0.627[our hospital], respectively. Stratification based on the cutoff value of the nomogram yielded low- and high-risk subgroups in four cohorts. For patients in the high-risk group, postoperative radiotherapy was considered a survival-promoting strategy (unadjusted HR 0.641, 95% CI 0.469-0.876, p = 0.0046). In the low-risk group, however, the implementation of radiotherapy barely had an influence on CSS. In conclusion, the nomogram we constructed and validated could predict the prognosis of SCLC patients followed surgery and identify high-risk patients who were likely to benefit from postoperative radiotherapy.
Objective: This study was to explore the difference and significance of parietal pleura invasion and rib invasion in pathological T classification with non-small cell lung cancer. Methods: A total of 8681 patients after lung resection were selected to perform analyses. Multivariable Cox analysis was used to identify the mortality differences in patients between parietal pleura invasion and rib invasion. Eligible patients with chest wall invasion were re-categorized according to the prognosis. Cancer-specific survival curves for different pathological T (pT) classifications were presented. Results: There were 466 patients considered parietal pleura invasion, and 237 patients served as rib invasion. Cases with rib invasion had poorer survival than those with the invasion of parietal pleura (adjusted hazard ratio [HR]= 1.627, P =0.004). In the cohort for parietal pleura invasion, patients with tumor size ≤5cm reached more satisfactory survival outcomes than patients with tumor size >5cm (unadjusted HR =1.598, P =0.006). However, there was no predictive difference in the cohort of rib invasion. The results of the multivariable analysis revealed that the mortality with parietal pleura invasion plus tumor size ≤5cm were similar to patients with classification pT3 (P =0.761), and patients for parietal pleura invasion plus tumor size >5cm and pT4 had no stratified survival outcome (P =0.809). Patients identified as rib invasion had a poorer prognosis than patients for pT4 (P =0.037). Conclusions: Rib invasion has a poorer prognosis than pT4. Patients with parietal pleura invasion and tumor size with 5.1-7.0cm could be appropriately up-classified from pT3 to pT4.
