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

Stereotactic body radiotherapy in lung cancer: a contemporary review

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.

A Dosimetric Comparative Study of Carbon-Ion Radiotherapy Versus X-ray Volumetric Modulated Arc Therapy for Stage III Non-Small-Cell Lung Cancer

BACKGROUND

Compared to photon beam, carbon-ion radiotherapy (CIRT) has both physical and biological advantages.

AIM

To examine whether two-dimensional (2D) CIRT is dosimetrically superior to photon beam volume-modulated arc therapy (VMAT) in protecting the normal tissues for stage III non-small-cell lung cancer (NSCLC).

SUBJECTS AND METHODS

A retrospective study was conducted. Thirteen patients with stage III NSCLC treated in our center with curative CIRT and a sham photon beam VMAT treatment planning with the same normal tissue dose constraints were included for analysis. Target dose distributions and the homogeneity index (HI) within the planning target volumes were compared.

RESULTS

Both CIRT and VMAT plans have good tumor coverage with no significant differences in D98, D95, and D50 of Planning target volume 1 (PTV1) between the two plans. The HIs between the two plans are similar. The HI of PTV2 is superior in the CIRT plan (CIRT vs. VMAT: 0.08 vs. 0.16, P < 0.05). In general, CIRT results in a lower dose of the organ-at-risk (OAR) than the photon plans. The V5, V10, V20, V30, V40, and Dmean of the contralateral lung in the CIRT plan are significantly lower than that of the photon VMAT. For the ipsilateral lung, the V5 of CIRT is significantly lower. The CIRT also had significantly lower spinal cord Dmax, esophageal Dmean and V50, V10 and V30 of bone, and V50 of the trachea and bronchial tree.

CONCLUSIONS

Compared with photon VMAT, 2D-CIRT using the passive beam scanning technique significantly reduces the radiation dose to the OARs in curative radiotherapy of stage III NSCLC, suggesting a better protection of the normal tissues.

Light dose effect of photodynamic therapy on growth inhibition and apoptosis induction in non-small cell lung cancer: A study in nude mouse model

BACKGROUND

Photodynamic therapy (PDT) is receiving increasing attention in treating non-small cell lung cancer (NSCLC) worldwide, but in clinical practice, the relationship between treatment effect and PDT light dose in NSCLC remains unclear. Therefore, we aimed to determine the optimal light dose for PDT by exploring molecular biomarkers and evaluating tumor growth data.

METHODS

We applied bioinformatics to identify promising genes and pathways in NSCLC and PDT. Then, the human lung adenocarcinoma cell line A549-bearing BALB/c nude mice were treated with hematoporphyrin derivative (HPD, 3 mg/kg) that is currently used widely for lung cancer treatment in the world even with photosensitization issues. After 48 h, tumor-bearing mice were irradiated superficially at doses of 100, 200, 300, 400, and 500 J/cm2. The tumor growth data and apoptotic molecules were assessed and calculated.

RESULTS

Bioinformatics results indicated that the apoptosis pathway was significantly enriched and caspase 3 was the most promising biomarker on prognosis in NSCLC-PDT. Compared to the untreated group, there was no difference in the relative tumor volume (RTV) of the 100 J/cm2 group, while the RTV of the other treatment groups (200-500 J/cm2) was significantly lower. In the 100 J/cm2 group, there were significant differences in the complete remission (CR, 0 %) and the percentage of tumor growth inhibition rate (TGI%) over 75 % (20 %) compared with the other treatment groups, especially the 300 and 400 J/cm2 groups (CR 70 %; TGI% 90 %). In the 300 and 400 J/cm2 groups, the expression of caspase 3, cleaved-caspase 3, PARP1, and Bax was increased significantly, while Bcl-2 expression was significantly lower.

CONCLUSIONS

Moderate doses of PDT (300 or 400 J/cm2) are more effective than low (100 or 200 J/cm2) or high doses (500 J/cm2) in the A549 tumor-bearing mice model. Since the A549 tumor is more akin to human tumors in pathological behavior, these experimental data may contribute to improving HPD-PDT illumination protocols for favorable clinical outcomes.

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

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.