Combination of Immunotherapy and Radiotherapy-The Next Magic Step in the Management of Lung Cancer?

none4sinoneHendriks L.E.L.; Menis J.; De Ruysscher D.K.M.; Reck M.Hendriks, L. E. L.; Menis, J.; De Ruysscher, D. K. M.; Reck, M

Management of stage I and II nonsmall cell lung cancer

The incidence of stage I and II nonsmall cell lung cancer is likely to increase with the ageing population and introduction of screening for high-risk individuals. Optimal management requires multidisciplinary collaboration. Local treatments include surgery and radiotherapy and these are currently combined with (neo)adjuvant chemotherapy in specific cases to improve long-term outcome. Targeted therapies and immunotherapy may also become important therapeutic modalities in this patient group. For resectable disease in patients with low cardiopulmonary risk, complete surgical resection with lobectomy remains the gold standard. Minimally invasive techniques, conservative and sublobar resections are suitable for a subset of patients. Data are emerging that radiotherapy, especially stereotactic body radiation therapy, is a valid alternative in compromised patients who are high-risk candidates for surgery. Whether this is also true for good surgical candidates remains to be evaluated in randomised trials. In specific subgroups adjuvant chemotherapy has been shown to prolong survival; however, patient selection remains important. Neoadjuvant chemotherapy may yield similar results as adjuvant chemotherapy. The role of targeted therapies and immunotherapy in early stage nonsmall cell lung cancer has not yet been determined and results of randomised trials are awaited

Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics

Introduction There is a cumulative risk of 20-40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT. Methods Gadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N = 1,404) and a validation cohort (N = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated. Results The best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient. Conclusion Machine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies

Invasive aspergillosis mimicking metastatic lung cancer

In a patient with a medical history of cancer, the most probable diagnosis of an (18)FDG-avid pulmonary mass combined with intracranial abnormalities on brain imaging is metastasized cancer. However, sometimes a differential diagnosis with an infectious cause such as aspergillosis can be very challenging as both cancer and infection are sometimes difficult to distinguish. Pulmonary aspergillosis can present as an infectious pseudotumour with clinical and imaging characteristics mimicking lung cancer. Even in the presence of cerebral lesions, radiological appearance of abscesses can look like brain metastasis. These similarities can cause significant diagnostic difficulties with a subsequent therapeutic delay and a potential adverse outcome. Awareness of this infectious disease that can mimic lung cancer, even in an immunocompetent patient, is important. We report a case of a 65-year-old woman with pulmonary aspergillosis disseminated to the brain mimicking metastatic lung cancer

Stage III Non-Small Cell Lung Cancer in the elderly: Patient characteristics predictive for tolerance and survival of chemoradiation in daily clinical practice

Background: In unselected elderly with stage III Non-Small Cell Lung Cancer (NSCLC), evidence is scarce regarding motives and effects of treatment modalities. Methods: Hospital-based multicenter retrospective study including unresectable stage III NSCLC patients aged >= 70 and diagnosed between 2009 and 2013 (N = 216). Treatment motives and tolerance (no unplanned hospitalizations and completion of treatment), and survival were derived from medical records and the Netherlands Cancer Registry. Results: Patients received concurrent chemoradiation (cCHRT, 33%), sequential chemoradiation (sCHRT, 24%), radical radiotherapy (RT, 16%) or no curative treatment (27%). Comorbidity, performance status (58%) and patient refusal (15%) were the most common motives for omitting cCHRT. Treatment tolerance for cCHRT and sCHRT was worse in case of severe comorbidity (OR 6.2 (95%Cl 1.6-24) and OR 6.4 (95%CI 1.8-22), respectively). One-year survival was 57%, 50%, 49% and 26% for cCHRT, sCHRT, RT and no curative treatment, respectively. Compared to cCHRT, survival was worse for no curative treatment (P = 0.000), but not significantly worse for sCHRT and RT (P = 0.38). Conclusion: Although relatively fit elderly were assigned to cCHRT, treatment tolerance was worse, especially for those with severe comorbidity. Survival seemed not significantly better as compared to sCHRT or RT. Prospective studies in this vital and understudied area are needed