GOECP/SEOR radiotheraphy guidelines for non-small-cell lung cancer

Dios, N. Rodriguez De; Navarro-Martín, Arturo; Cigarral, C.; Chicas-Sett, Rodolfo; García, Rafael; García, Virginia; González, José A.; Gonzalo, Susana; Murcia-Mejía, M.; Robaina, Rogelio; Sotoca, Amalia; Vallejo, Carmen; Valtueña, G.; Couñago, Felipe

doi:10.5306/wjco.v13.i4.237

Cited by 11 publications

(16 citation statements)

References 200 publications

(185 reference statements)

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“…3 m. In terms of the additional treatment features, combined chemo- and radiotherapy had a statistically significant positive impact on survival, while radiotherapy alone had a statistically significant negative impact (in both cases p < 0.05, Wald test, two-sided). We speculate that the latter is not an effect of the therapy itself, but rather of the patients’ clinical state in general, which might have necessitated the use of this method of treatment [ 60 ]. Additionally, as expected, both chemotherapy and targeted molecular therapy were positive survival predictors (Sup.…”

Section: Resultsmentioning

confidence: 99%

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer

et al. 2022

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Background Despite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world – lung cancer, their interrelations are not well understood. Digital pathology data provides a unique insight into the spatial composition of the TME. Various spatial metrics and machine learning approaches were proposed for prediction of either patient survival or gene mutations from this data. Still, these approaches are limited in the scope of analyzed features and in their explainability, and as such fail to transfer to clinical practice. Methods Here, we generated 23,199 image patches from 26 hematoxylin-and-eosin (H&E)-stained lung cancer tissue sections and annotated them into 9 different tissue classes. Using this dataset, we trained a deep neural network ARA-CNN. Next, we applied the trained network to segment 467 lung cancer H&E images from The Cancer Genome Atlas (TCGA) database. We used the segmented images to compute human-interpretable features reflecting the heterogeneous composition of the TME, and successfully utilized them to predict patient survival and cancer gene mutations. Results We achieved per-class AUC ranging from 0.72 to 0.99 for classifying tissue types in lung cancer with ARA-CNN. Machine learning models trained on the proposed human-interpretable features achieved a c-index of 0.723 in the task of survival prediction and AUC up to 73.5% for PDGFRB in the task of mutation classification. Conclusions We presented a framework that accurately predicted survival and gene mutations in lung adenocarcinoma patients based on human-interpretable features extracted from H&E slides. Our approach can provide important insights for designing novel cancer treatments, by linking the spatial structure of the TME in lung adenocarcinoma to gene mutations and patient survival. It can also expand our understanding of the effects that the TME has on tumor evolutionary processes. Our approach can be generalized to different cancer types to inform precision medicine strategies.

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Section: Resultsmentioning

confidence: 99%

Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer

et al. 2022

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“…The guidelines recommend SBRT treatment for less than 5 cm lung lesions, but tumors >5 cm can also be treated with appropriate care [58]. Comparing lobectomy and SBRT in the case of operable patients, previous studies have produced similar results [26], but prospective phase 2-3 studies are still ongoing (VALOR, POSTILV, STABLE-MATES) [16].…”

Section: Discussionmentioning

confidence: 99%

“…Based on the patient’s suitability for surgery, the American College of Chest Physicians practice guidelines categorized lung tumor patients into standard risk, high risk, or inoperable categories [ 15 ]. 25% of lung tumor patients diagnosed at an early stage are medically inoperable [ 16 ]. The exact definition is variable by studies.…”

Section: Sbrt Vs Conventional Radiotherapy For Early-stage Nsclcmentioning

confidence: 99%

“…The results of this trial may help us in the future [ 28 ]. Another prospective trial that is still in progress and is scheduled to be completed in 2026 is the POSTILV phase 2 study, where radical resection is compared with SBRT for stage I patients, and one of the aims of the study is to assess whether SBRT, with the correct dose and technique, more effective than surgery [ 16 ]. The STABLE-MATES phase 3 trial will be completed in 2024, comparing sublobar resection with SBRT in high-risk operable patients [ 16 ].…”

Section: Sbrt Vs Conventional Radiotherapy For Early-stage Nsclcmentioning

confidence: 99%

“…It will investigate the role of FDG PET-CT in monitoring tumor progression and assessing side effects [ 30 ]. Based on the evidence presented so far, the maximum 50–60 Gy seems to be optimal for the fractionation scheme of centrally located lung tumors, delivered in 5 fractions, but considering the nearby OARs, 8 × 7.5 Gy is also a suitable option [ 16 ].…”

Section: Sbrt Vs Conventional Radiotherapy For Early-stage Nsclcmentioning

confidence: 99%

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Stereotactic body radiotherapy in lung cancer: a contemporary review

Csiki,

Simon,

Papp

et al. 2024

Pathol. Oncol. Res.

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The treatment of early stage non-small cell lung cancer (NSCLC) has improved enormously in the last two decades. Although surgery is not the only choice, lobectomy is still the gold standard treatment type for operable patients. For inoperable patients stereotactic body radiotherapy (SBRT) should be offered, reaching very high local control and overall survival rates. With SBRT we can precisely irradiate small, well-defined lesions with high doses. To select the appropriate fractionation schedule it is important to determine the size, localization and extent of the lung tumor. The introduction of novel and further developed planning (contouring guidelines, diagnostic image application, planning systems) and delivery techniques (motion management, image guided radiotherapy) led to lower rates of side effects and more conformal target volume coverage. The purpose of this study is to summarize the current developments, randomised studies, guidelines about lung SBRT, with emphasis on the possibility of increasing local control and overall rates in “fit,” operable patients as well, so SBRT would be eligible in place of surgery.

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