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C.H. Kim
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MINI 25 - Trials, Radiation and Other (ID 142)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Thymoma, Mesothelioma and Other Thoracic Malignancies
- Presentations: 1
- Moderators:J.M. Clavero, R. Hassan
- Coordinates: 9/08/2015, 16:45 - 18:15, 702+704+706
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MINI25.13 - Survival Prediction Model of Repeated Pulmonary Metastasectomy for Osteosarcoma: A Nomogram and Regression Trees (ID 2876)
17:55 - 18:00 | Author(s): C.H. Kim
- Abstract
- Presentation
Background:
Surgical resection for pulmonary metstasis of osteosarcoma has been considered as the treatment of choice, however, it was not feasible to predict the benefit of metastasectomy for patients with multiple poor prognostic parameters. Survival prediction model can be very helpful for this purpose, so we made a nomogram based on parametric survival model(PSM) and regression trees from recursive partitioning analysis(RPA).
Methods:
We reviewed the clinical variables of patients who underwent single or multiple surgical resection for pulmonary metastasis of osteosarcoma between 1994 and 2012. Prognostic parameters were incorporated into PSM and RPA to build a nomogram and regression trees for the prediction of survival after single or multiple metastasectomy. The ‘rms’ and ‘rpart’ package of R(version 3.2.0) were used for this procedure. PSM was validated with C-index calculated by bootstrap method and then the parameters of PSM were used for RPA.
Results:
We analyzed 186 patients who received 294 metastasectomies. The number of second, third, and forth metastasectomy cases were 62, 28, and 11 respectively. Overall 5-year survival rate after first metastasectomy was 47%. Age, gender, number of metastatic nodules, frequency of metastasectomy, disease free interval before metastasectomy, size, subtype and resection margin of primary tumor were affecting overall survival. Nomogram and regression trees were displayed in figures. C-index of PSM was 0.71. Figure 1 Figure 2
Conclusion:
Our prediction model using a nomogram and regression trees can be easily employable for calculating survival benefits. Nomogram and RPA are complementary to each other. RPA displays comprehensive grouping of patients who have similar prognosis, while nomogram is useful for predicting hazard ratio of individual patient. In this study, our combined model constitutes a useful tool for predicting prognosis of patients who undergo repeated metastasectomy for osteosarcoma.
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P3.02 - Poster Session/ Treatment of Localized Disease – NSCLC (ID 211)
- Event: WCLC 2015
- Type: Poster
- Track: Treatment of Localized Disease - NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P3.02-010 - Tumor Recurrence of the Chest Wall after Percutaneous Hook Wire Localization (ID 1300)
09:30 - 09:30 | Author(s): C.H. Kim
- Abstract
Background:
Increasingly, localization of small lung nodule (solid or ground glass) is needed for thoracoscopic resection of accurate diagnostic and/or curative intent. Hook wire implantation is one of important localization techniques. Meanwhile, tumor recurrence in the chest wall of the percutaneous FNA tract is well known in thoracic malignancy, particularly lung cancer.
Methods:
We report the case of a 64-year-old-man with tumor recurrence of the chest wall. Eight months earlier, he underwent hook wire-guided thoracoscopic resection of RUL nodule and further anterior segmentectomy because of intraoperative diagnosis of NSCLC (squamous cell carcinoma, pT1aN0M0 IA).
Results:
Location of the chest wall tumor was coincident with the hook wire tract. The tumor was resected en-bloc, and reported as a metastatic squamous cell carcinoma. Figure 1Figure 2
Conclusion:
To reduce the risk of the tumor recurrence related with localization techniques, thoracic surgeons had better know very well the topographical anatomy of lung and avoid an unnecessary localization technique, and the wire is recommended to be withdrawn through the VATS port rather than percutaneously. This is the first report of tumor recurrence related with hook wire localization in the PubMed search.
