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Marta Scorsetti
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CS01 - Controversies in NSCLC OMD (ID 3)
- Event: WCLC 2019
- Type: Controversy Session
- Track: Oligometastatic NSCLC
- Presentations: 1
- Now Available
- Moderators:Tomoyuki Hishida, Yolande Lievens
- Coordinates: 9/09/2019, 11:00 - 12:30, Colorado Springs (1994)
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CS01.03 - Technical Reasons of Local Treatment Define the Limits of NSCLC OMD in Terms of Number of Metastases, Not a Fixed Number (Now Available) (ID 3146)
11:00 - 12:30 | Presenting Author(s): Marta Scorsetti
- Abstract
- Presentation
Abstract
Currently there is no consensus on the definition of oligometastatic disease in NSCLC, with 3 or 5 lesions historically considered as the upper limit [1]. A low number of metastases, indeed, is a good although not perfect surrogate of the biology behind the oligometastatic state. In real life practice, the number of metastatic lesions is often misleading, since it is possible to find patients with more than 5 metastases affected by a slowly progressing disease, potentially taking advantage from local treatments. On the contrary, patients affected by just one or two metastases can progress very rapidly with a dismal prognosis, despite the apparent low disease burden.
Since the number of metastases is not a perfect indicator of oligometastatic state and biomarkers really able to identify this disease are lacking, there is a trend favoring the technical feasibility of local treatment over the number of metastases to treat. This approach has pros and cons. On one side, the idea of killing all visible cancer cells independently by their number is appealing and possibly with a positive impact on patient prognosis. On the other side, clinical data supporting such an aggressive local treatment have still a low level of evidence. Moreover, the definition of “technically feasible” is quite vague, particularly in the world of radiation oncology. Indeed, radiotherapy is strongly related to technological development. The innovations in this setting have dramatically increased the possible indications of radiotherapy, also for oligometastases. With state of the art radiotherapy, we are now able to treat virtually all sites in the body and it is becoming really difficult to define an upper limit to the number of lesions that can be treated. However, this is feasible only with advanced technologies, like image guided radiotherapy (IGRT), motion management (4D CT, gating, tracking, etc.), and heavy particles in particular clinical settings (retreatment for instance). This trend is creating a gap between Radiation Therapy Departments, since some treatments are becoming safely deliverable only in well selected Institutions with high expertise in this field.
Despite all recent technological achievements, some clinical settings remain in which the risk-benefit ratio should be carefully weighted before delivering ablative dose to a metastatic patient. For instance, there are still uncertainties in the treatment of central lung lesions abutting on the main bronchus [2] or, changing scenario, the amount of remaining healthy liver is still limiting liver metastases treatment in some situations [3]. More importantly, the goal of local treatment of an oligometastatic patient should be to change the natural history of the tumor, independently from the number of metastases we are able to treat. Treating all the metastases, even though safely feasible, remains just a technical exercise if no impact on prognosis, quality of life or symptoms control is achievable. Oligometastatic disease has definitely a different biology, and every effort should be in the direction of identifying this biology [4]. Technologies have developed faster than our clinical and biological knowledge, and this should be kept in mind.
In conclusion, the number of metastases remains a good clinical indication of oligometastatic state, but this number should not be an insuperable limit in clinical practice. Technical feasibility of local treatments (as radiotherapy) should be always carefully weighted accounting for risk-benefit ratio. Being able to treat any number of metastases should not be considered as a good reason for doing it indiscriminately. Physicians should always consider the clinical and biological reasons for a local ablative treatment in a metastatic patient, independently by technical issues.
[1] Hong JC, Salama JK. The expanding role of stereotactic body radiation therapy in oligometastatic solid tumors: What do we know and where are we going? Cancer Treatment Reviews 52 (2017) 22–32
[2] Videtic GM, Donington J, Giuliani M et al. Stereotactic body radiation therapy for early stage non-small cell lung cancer: Executive Summary of an ASTRO Evidence-Based Guideline. Practical Radiation Oncology (2017) 7, 295-301
[3] Mondlane G, Ureba A, Gubanski M et al. Estimation of the risk for radiation-induced liver disease following photon- or proton-beam radiosurgery of liver metastases. Radiat Oncol. 2018 Oct 22;13(1):206. doi: 10.1186/s13014-018-1151-6
[4] Correa RJ, Salama JK, Milano MT et al. Stereotactic Body Radiotherapy for Oligometastasis Opportunities for Biology to Guide Clinical Management. Cancer J. 2016 Jul-Aug;22(4):247-56.
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P2.17 - Treatment of Early Stage/Localized Disease (ID 189)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Treatment of Early Stage/Localized Disease
- Presentations: 1
- Now Available
- Moderators:
- Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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P2.17-20 - A Radiomic Approach to Predict Nodal and Distant Relapse in Patients Treated with Stereotactic Body Radiation Therapy for Early Stage NSCLC (Now Available) (ID 2178)
10:15 - 18:15 | Author(s): Marta Scorsetti
- Abstract
Background
Regional and distant relapse remain a significant issue in the treatment of early stage non small cell lung cancer with Stereotactic Body Radiation Therapy (SBRT). There is a need for predictive biomarkers able to identify patients that are at higher risk of relapse. In this work we present a radiomic approach using features extracted by routine planning CT, to predict the risk of nodal and distant recurrence.
Method
A cohort of 102 patients was retrospectively investigated. All patients were affected by early stage (T1-T2) lung cancer and received the same radiation treatment with 48Gy delivered in 4 fractions. For all patients, a set of 45 radiomics textural features was computed for the tumor volumes segmented on the treatment planning CT images. Patients were split into two independent cohorts used for training (70% of cases) and validation (30% of cases). A stepwise backward linear discriminant analysis (LDA) was applied as a classifier to identify patients at risk of lymph-nodal progression. The performance of the model was assessed by means of standard metrics derived from the confusion matrix. Furthermore, all textural features were correlated to survival data to build predictive models: the features/predictors found significant at univariate analysis and to elastic net regularization, were included in a multivariate model to predict disease specific progression free survival (PFS) and disease specific survival (DS OS). Low and high risk groups were identified by maximizing the separation by means of the Youden method.
Result
In the total cohort (77 (75.5%) males and 25 (24.5%) females, median age 76.6 years), 15 patients presented nodal progression at the time of analysis (11 in the training and 4 in the validation sets); 19 patients (18.6%) died because of disease specific causes, 25 (24.5%) died for other reasons, 28 (27.5%) were alive without disease and 30 (29.4%) with either local or distant progression. The mean tumor volume was 5.6±6.4cm3. Figure 1 illustrates the actuarial curves for PFS and DS OS over the entire training and test cohorts (in both cases the difference was not significant) and the same data stratified in low and high risk groups identified. In all case highly significant differences were identified.
Conclusion
Radiomics features extracted from treatment planning CT images can distinguish patients with low and high risk of tumor progression and disease specific death in early stage lung cancer treated with SBRT.
