Background: Molecular characterization of metastatic lung adenocarcinomas is mandatory but might be hampered by the quantity of tissue, restricted access to molecular platforms or limited economical resources. Our aim was to develop a tool supported by the hypothesis that radiological patterns of pts could help predict the rate of positivity of the most common oncogenic drivers. Methods: We defined an algorithm based on a molecularly defined cohort of 656 pts with stage IV lung adenocarcinoma. Two radiologists centrally reviewed the baseline imaging. Clinical data were retrospectively collected. There were 135 EGFR mutations, 81 ALK fusions, 47 BRAF mutations, 141 KRAS mutations, and 146 pan-negative tumors for these 4 oncogenic drivers. Univariate correlation analyses were performed to define an algorithm predicting the molecular testing positivity based on the metastatic pattern. Subsequently, an online tool was developed. This study was approved by our institutional review board. Results: Metastatic patterns correlated with the genomic drivers when compared to the pan-negative group. In the EGFR group, pleural metastases were more frequent (32% vs. 20%; p = 0.021), whereas adrenal and node metastases less frequent (6% vs.23%; p < 0.001 and 11% vs. 23% respectively; p = 0.011). In the ALK group, there were more brain and lung metastases (respectively 42% vs. 29%; p = 0.043 and 37% vs. 24% respectively; p = 0.037). In the BRAF group, pleural and pericardial metastases were more common (47% vs. 20%; p < 0.001 and 11% vs. 3% respectively; p = 0.04) and bone metastases less common (21% vs. 42%; p = 0.011). Lymphangitis was more frequent in EGFR, ALK and BRAF groups (6%, 7% and 15% vs. 1%; p = 0,016, p = 0,009 and p < 0,001 respectively). A free online access to the algorithm is now available after registration at http//tactic-ct.fr. Physicians enter age, sex, smoking status and the sites of metastases at diagnosis (present/absent/unknown). A mutation score is calculated, reflecting the % of chance to find an oncogenic driver. On the website, contributors can also enter new cases and an artificial intelligence will refine the algorithm and expand the number of oncogenic drivers. Conclusions: Our free access tool allows establishing a hierarchy in the molecular testing based on simple clinical and radiological information. Continual learning from new cases entered in the database will increase the sensitivity of the tool. This tool might save time, tumor tissue, economical resources and accelerate access to personalized treatment.