AIRIaL: Artificial Intelligence and Resistance Imaging in Lung Cancer

肺癌是全世界最常见的癌症死亡原因(每年约160万人死亡)。不幸的是，在所有患有肺癌的人中，100人中只有5人在确诊后活了10年。存活率低的主要原因是标准的肺癌治疗失败，因为存在对这些治疗无效的癌症。在这个在小鼠身上进行的早期项目中，我们将开发新的医学成像测试来检测肺癌的治疗耐药性。使用这种成像测试，我们可以预测癌症是否会对处方的治疗产生反应，如果是，效果如何。在未来，及早了解患者对治疗的反应将使临床团队能够在必要时切换到替代选择。这将避免患者接受对他们没有好处的潜在毒性治疗。目前，医生必须在治疗开始后大约3个月进行CT扫描，才能知道它是否有效。我们将创建一个计算机程序(使用人工智能)，该程序能够自动读取肺部扫描结果，并更准确地预测癌症是否对治疗具有抵抗力。然后，我们将使用工程材料将药物只输送到耐药的癌细胞。在这个阶段，我们将在老鼠身上测试这些新药和计算机程序，看看这种新方法是否有效。通过将药物仅用于癌症本身，我们希望减少患者的副作用，并显著改善治疗效果。最后，我们将确定我们的计算机程序是否可以随着时间的推移监测癌症的变化，这将提供对治疗耐药性如何发生的洞察，因为目前还不清楚这一点。最终，我们希望这个项目的结果将使医生能够为个别患者量身定做肺癌的治疗方法。此外，这可能会导致这种毁灭性疾病的新疗法的开发。

Lung cancer is the most common cause of cancer death world-wide (~1.6 million deaths/year). Unfortunately, of all people who have lung cancer, only 5 out of 100 are living ten years after diagnosis. The main reason for this low survival rate is that standard lung cancer therapies fail due to the presence of cancer that does not respond to these treatments. In this early-stage project performed in mice, we will develop new medical imaging tests to detect therapy resistance in lung cancer. Using this imaging test, we can predict if the cancer will respond to the prescribed treatment, and if so, how well. In the future, knowing early on about how a patient will respond to treatment will enable the clinical team to switch to alternative options if necessary. This will avoid patients being given potentially toxic treatments that will not benefit them. At present doctors have to wait for CT scans about 3 months after treatment starts to be able to tell if it is working or not. We will create a computer programme (using artificial intelligence) which is able to automatically read lung scans and more accurately predict whether the cancer is resistant to treatment. Then, we will use engineered materials to deliver drugs only to the resistant cancer cells. At this stage we will test these new drugs and computer program in mice to see if this new approach is effective. By delivering the drug just to the cancer itself, we hope to reduce side effects for patients and significantly improve how well the treatment works. Finally, we will determine if our computer programme can monitor changes in cancer over time, which will provide insight into how treatment resistance occurs as this is currently not well-understood. Ultimately, we hope that the results of this project will enable doctors to tailor the treatment for lung cancer to the individual patient. Moreover, it might lead to the development of new treatments for this devastating disease.