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Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images.
Author: DokiYuichiro, EguchiHidetoshi, FujinoShiki, HamabeAtsushi, HataTsuyoshi, HayashiRie, KagawaYoshinori, KatoShinya, MinamiSoichiro, MiyoshiNorikatsu, NagaeAyumi, OginoTakayuki, SekidoYuki, TakahashiHidekazu, TeiMitsuyoshi, UemuraMamoru, YamamotoHirofumi
Original Abstract of the Article :
In current clinical practice, several treatment methods, including neoadjuvant therapy, are being developed to improve overall survival or local recurrence rates for locally advanced rectal cancer. The response to neoadjuvant therapy is usually evaluated using imaging data collected before and after...See full text at original site
Dr.Camel's Paper Summary Blogラクダ博士について
ラクダ博士は、Health Journal が論文の内容を分かりやすく解説するために作成した架空のキャラクターです。
難解な医学論文を、専門知識のない方にも理解しやすいように、噛み砕いて説明することを目指しています。
* ラクダ博士による解説は、あくまで論文の要点をまとめたものであり、原論文の完全な代替となるものではありません。詳細な内容については、必ず原論文をご参照ください。
* ラクダ博士は架空のキャラクターであり、実際の医学研究者や医療従事者とは一切関係がありません。
* 解説の内容は Health Journal が独自に解釈・作成したものであり、原論文の著者または出版社の見解を反映するものではありません。
引用元：
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10551859/
データ提供：米国国立医学図書館（NLM）
Predicting Treatment Response for Rectal Cancer Using Colonoscopy Images
The realm of cancer treatment is constantly evolving, and this study explores a groundbreaking approach to predicting the effectiveness of neoadjuvant chemotherapy for rectal cancer. By leveraging the power of deep learning and analyzing colonoscopy images, this research aims to provide personalized treatment strategies for patients. The authors constructed a deep learning model that can analyze colonoscopy images and predict the response to neoadjuvant chemotherapy.
Deep Learning Revolutionizes Cancer Treatment
This research demonstrates the potential of deep learning to revolutionize cancer treatment. By accurately predicting the response to neoadjuvant chemotherapy, it empowers healthcare professionals to tailor treatment plans, potentially leading to better outcomes for patients. The model achieved a sensitivity, specificity, accuracy, positive predictive value, and area under the curve of 77.6%, 62.9%, 71.4%, 74.5%, and 0.713, respectively, in predicting a poor response to neoadjuvant therapy.
The Promise of Personalized Medicine
This study shines a light on the transformative potential of personalized medicine. By harnessing the power of deep learning and analyzing individual patient data, healthcare professionals can make more informed decisions about treatment, leading to more effective and personalized care. This research provides a compelling example of how technology can empower us to better understand and combat disease.
Dr. Camel's Conclusion
Imagine a vast desert landscape, with each grain of sand representing a patient's unique characteristics. Deep learning allows us to sift through this intricate landscape, identifying patterns and predicting outcomes. This research is a step towards personalized medicine, where treatments are tailored to the individual, like a bespoke desert oasis, offering optimal comfort and healing.
Date :
- Date Completed n.d.
- Date Revised 2023-10-10
Further Info :
Related Literature
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