由 IPM 員工撰寫December 14, 2023 4:17 AM (GMT+7)December 14, 2023 4:17 AM (GMT+7)
https://www.insideprecisionmedicine.com/topics/oncology/eric-topol-provides-his-vision-of-how-ai-can-redefine-cancer-screening-and-diagnosis/
Eric Topol, MD, founder and director of the Scripps Research Translational Institute, recently provided a keynote interview for the AI in Precision Oncology virtual event in which he described his vision of how AI can help redefine cancer screening and diagnosis. Topol was interviewed by Doug Flora, MD, editor in chief of the journal AI in Precision Oncology from Mary Ann Liebert Inc., which sponsored the presentation. Below is a small selection of the interview on the role AI will play in the future of clinical cancer care.
Doug Flora, MD, Editor in Chief, AI in Precision Medicine: Can you speak a little bit about changing the way we think about screening cancers, using more polygenic risk score and more AI-driven algorithm?
Eric Topol, MD, founder and director of the Scripps Research Translational Institute: I’m so glad you brought that up, because that is something that I feel so strongly about—that we’ve got this all wrong. That is, we’re only picking up 12%, 14% of all the cancers that are being diagnosed—important cancers—through our mass screening. It is wasting tens of billions, if not hundreds of billions dollars every year. It’s inducing a lot of anxiety for all the false positives, like as in mammography, but also other screening. And it’s all based on age, which is so dumb.
Now, when you start to think about the fact that cancer is occurring in younger people, much more commonly now, people in their 20s are coming across with colon cancer, and women with breast cancer in your 30s. Well, if you just use the current (cancer screening) criteria, we’re going to miss these people. And this is of course, not acceptable. So is there a better way—and I am convinced there really is—and we should be going after it. And that is there’s layers of data, which would define the risk of each individual. We’ve already seen, just for pancreatic cancer, using datasets from all the country of Denmark and the U.S. veterans dataset, that you can pick up pancreatic cancer risk from the notes, from the notes, lab tests, things where we wouldn’t see the trend. Then you start getting unstructured text, you start putting in polygenic risk scores, which are very inexpensive to obtain and we have those for most of common cancers, no less than cancer predisposition genes that are easily obtained. So we can define risk. And with AI, picking up things in images that we can’t see.
So if we start to reboot how we do cancer screening, I think we’re going to get to a point where we can narrow down the field. For example, 88% of women will never develop breast cancer. Why do those women need to have mammography every year or two? So let’s get this done. Let’s define risk and let’s not miss young people who are at risk for cancer. You know, we have things like the cell free tumor DNA test that we can use, we have clonal hematopoiesis that we’re not using, chip tests that you can get even through sequencing. So there’s lots of ways we can do this, but we can’t be complacent about how we do screening now because it isn’t working. It’s wasteful. The cancers that are being picked up waiting for some symptoms or scans to be so abnormal, they’re often late. We’re not changing the natural history of cancer. We’ve got to get better at that too.
Flora: We’ve spent most of the last couple decades refining screening for one individual organ cancer. Certainly, there are AI tools that are starting to identify areas that might need attention from an endoscopist on colonoscopies. You’ve written extensively about these pattern doctors being outpaced now by machine learning and by training these machine learning modules to identify things in their faintest footsteps. Let’s touch on pattern doctors and what these tools are able to do now.
Topol: Well, you know what’s interesting is that gastroenterologists have led the field of AI, doing randomized trials. There was very recently 33 randomized trials from all around the world, many from China. But now, most places around the world have done randomized trials and, uniformly, the pickup of polyps is substantially better when machine vision, (in) real time is being used during the colonoscopy. Interestingly, there are also studies to show that as the day goes on, the gastroenterologist is more likely to miss those polyps. Now, we haven’t seen a paper yet, of course, that shows by picking up this significantly higher rate of adenomatous polyps, that it changes the natural history of cancer. But that, I think, is pretty likely.
We’ve already seen 80,000 women randomized with mammography with AI or without AI and the AI helped tremendously in accuracy of diagnosis and reducing the time of review of the scan. So we’re seeing some great, compelling evidence for the benefit of the patterns. And I would extend that because we’re talking about cancer to pathology slides. It’s amazing that from a whole slide image, you could get the driver mutations, the structural variations that are in play, whether it is actually a malignancy, or the primary source of that tumor, and even the prognosis from the side to a reasonable level of accuracy. We’re not using that. We still are in the mode of pathologists that are not in agreement about what that H&E slide shows. So we can do better with patterns of slides and of all kinds of medical imaging.
Flora: (Let’s speak about) the Internet of Things and these smart hospitals and homes that you’ve referred to in a couple of your Substacks. As we move into the future of medicine, where do you see this going in the next two years?
Topol: In the next couple of years I’m hoping that we’ll start to see cancer screening get upended. We won’t have it finalized, but at least some of the trials are ongoing now to challenge the old way of doing cancer screening. I think that there’s a lot of moving parts here. We will get a diagnosis improved, whether it’s because the accuracy of scan interpretation in the next couple of years, or whether it’s because each doctor, through their health system practice, has access to a GPT support that gives them a differential diagnosis of difficult diagnoses. So they’re at least thinking of things that they couldn’t.
We have to get rid of the rush job, of course, because if you only have seven minutes for a routine visit, that’s not enough to hear about a patient’s concerns and to think. So one of the things we have to work on in these next few years is not let the AI make things worse, not let more patients get squeezed into any daily schedule. That’s a challenge because we’ve got a lot of non-physician overlords out there that are making the call as to “Oh, well, you’re more efficient now. Let’s, let’s get your schedule filled up even more.” These are things we have to confront in the next couple of years that are really important. Because this is a very big, if not the most extraordinary, transformation of medicine that we’ll see in our lifetime.
But we have to plan ahead. What’s going to be the big factors. There will be tools to summarize every aspect of a patient’s data before you even start to look at their chart, before you go see the patient. They will be ready in the next couple of years and not just adding to the medical diagnosis accuracy. So the medical literature, it’s very hard for us to keep up. Two years from now, don’t worry about that. It’ll keep up for you. It’ll get you the daily skinny, if you want, on everything in your field. Because the corpus of medical literature for you is something that is right in the sweet spot of generative AI.
News & FeaturesArtificial intelligenceBreast cancerCancer screeningColon cancerDiagnostic imagingPancreatic cancerPolygenic risk scores
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埃里克·托波爾 Eric Topol 醫學博士，斯克里普斯研究轉化研究所的創始人兼主任，最近在精準腫瘤學虛擬活動中提供了一次主題演講，描述了他對人工智慧如何幫助重新定義癌症篩查和診斷的願景。托波爾接受了道格·弗洛拉醫學博士的採訪，弗洛拉是《精準腫瘤學中的人工智慧》期刊的主編，該期刊由瑪麗·安·利伯特公司贊助。以下是關於人工智慧在未來臨床癌症護理中所扮演角色的訪談節選。
道格·弗洛拉醫學博士，精準醫學中的人工智慧主編：您能否談談改變我們對癌症篩查的思維方式，使用更多的多基因風險評分和更多的人工智慧驅動算法？
Eric Topol 醫學博士，Scripps 研究轉化研究所創辦人兼主任：我很高興你提到這一點，因為這是我非常強烈的感受——我們完全搞錯了。也就是說，我們通過大規模篩查只檢測到 12%、14%的所有被診斷出的癌症——重要的癌症。這每年浪費了數百億甚至數千億美元。這給所有的假陽性結果帶來了很多焦慮，比如乳房 X 光檢查，但也包括其他篩查。而且這一切都是基於年齡，這真是太愚蠢了。
現在，當你開始思考癌症在年輕人中發生的事實時，這種情況現在更為普遍，二十多歲的人患上結腸癌，三十多歲的女性患上乳腺癌。如果我們僅使用現有的（癌症篩查）標準，我們將錯過這些人。而這當然是不可接受的。所以是否有更好的方法——我確信真的有——我們應該去追求它。那就是有層次的數據，可以定義每個人的風險。我們已經看到，僅僅針對胰腺癌，使用來自丹麥全國和美國退伍軍人數據集的數據，你可以從筆記、實驗室測試中發現胰腺癌風險，這些地方我們通常看不到趨勢。然後你開始獲取非結構化文本，開始引入多基因風險評分，這些評分獲取成本非常低，我們對大多數常見癌症都有這些評分，更不用說容易獲得的癌症易感基因。因此，我們可以定義風險。並且通過人工智能，從圖像中捕捉我們看不到的東西。
所以，如果我們開始重新啟動癌症篩檢的方法，我認為我們會達到一個可以縮小範圍的地步。例如，88%的女性永遠不會罹患乳癌。為什麼這些女性需要每年或每兩年做一次乳房 X 光檢查呢？所以讓我們來完成這件事。讓我們定義風險，並且不要錯過那些有癌症風險的年輕人。你知道，我們有像是無細胞腫瘤 DNA 測試這樣的工具可以使用，我們有克隆性造血現象但我們沒有使用，還有你甚至可以通過測序獲得的芯片測試。所以我們有很多方法可以做到這一點，但我們不能對目前的篩檢方式感到自滿，因為它並不奏效。這是浪費。那些等待出現一些症狀或掃描結果異常的癌症，通常已經是晚期。我們並沒有改變癌症的自然歷史。我們也必須在這方面做得更好。
Flora：在過去的幾十年裡，我們大部分時間都在改進針對單一器官癌症的篩查。當然，現在有一些人工智慧工具開始識別在結腸鏡檢查中可能需要內視鏡醫生注意的區域。你曾廣泛撰寫過這些模式醫生現在被機器學習超越的情況，並通過訓練這些機器學習模組來識別它們最微弱的跡象。讓我們來談談模式醫生以及這些工具現在能夠做到的事情。
Topol：嗯，你知道有趣的是，腸胃科醫生在人工智慧領域中處於領先地位，進行了隨機試驗。最近有來自世界各地的 33 項隨機試驗，其中許多來自中國。但現在，世界上大多數地方都已經進行了隨機試驗，並且一致地發現，在結腸鏡檢查過程中使用機器視覺（即時）時，息肉的檢出率顯著提高。有趣的是，還有研究顯示，隨著一天的進行，腸胃科醫生更有可能錯過這些息肉。當然，我們還沒有看到一篇論文顯示通過顯著提高腺瘤性息肉的檢出率會改變癌症的自然史。但我認為這是很有可能的。
我們已經看到有 80,000 名女性被隨機分配進行有 AI 或無 AI 的乳房 X 光檢查，AI 在診斷的準確性和減少掃描審查時間方面提供了極大的幫助。因此，我們看到了這些模式帶來的巨大、令人信服的證據。我會延伸這一點，因為我們在談論癌症到病理切片。令人驚訝的是，從整個切片圖像中，你可以獲得驅動突變、結構變異，無論它是否真的是惡性腫瘤，或是該腫瘤的主要來源，甚至是預後，達到合理的準確水平。我們還沒有使用這些技術。我們仍然處於病理學家對 H&E 切片顯示內容意見不一致的模式中。因此，我們可以通過切片模式和各種醫學影像做得更好。
芙蘿拉：（讓我們談談）物聯網以及你在幾篇 Substack 文章中提到的這些智慧醫院和家庭。隨著我們進入醫學的未來，你認為在接下來的兩年中這會走向何方？
Topol：在接下來的幾年裡，我希望我們會看到癌症篩檢被顛覆。我們不會有最終的結果，但至少一些挑戰舊有癌症篩檢方式的試驗正在進行中。我認為這裡有很多變動的部分。我們將會改進診斷，不論是因為在接下來的幾年裡掃描解釋的準確性提高，還是因為每位醫生通過他們的健康系統實踐，能夠獲得一個提供困難診斷的鑑別診斷的 GPT 支持。所以他們至少會考慮到他們以前無法想到的事情。
我們當然必須擺脫匆忙的工作，因為如果你只有七分鐘的例行訪問時間，那是不夠的，無法聽取病人的擔憂並進行思考。因此，在接下來的幾年裡，我們必須努力的一件事就是不要讓人工智慧使情況變得更糟，不要讓更多的病人被擠進每天的日程表。這是一個挑戰，因為我們有很多非醫生的上司在那裡做決定，比如「哦，好吧，你現在更有效率了。讓我們把你的日程安排得更滿。」這些是我們在接下來的幾年裡必須面對的非常重要的事情。因為這是我們一生中將看到的醫學領域中非常大，甚至是最非凡的變革。
但我們必須提前計劃。什麼會是主要因素。將會有工具在你開始查看病人的病歷之前，總結病人數據的每一個方面，這些工具在未來幾年內就會準備好，不僅僅是增加醫學診斷的準確性。因此，醫學文獻對我們來說很難跟上。兩年後，不用擔心這個問題。它會為你跟上。它會每天為你提供你領域內的最新資訊，因為對你來說，醫學文獻的語料庫正好處於生成式人工智慧的甜蜜點。