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Using Multimodal AI to Predict Lung ...

Ding, Ruiwen,

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Using Multimodal AI to Predict Lung Adenocarcinoma Recurrence /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using Multimodal AI to Predict Lung Adenocarcinoma Recurrence // Ruiwen Ding.
Author:	Ding, Ruiwen,
Description:	1 electronic resource (166 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Bioengineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32046688
ISBN:	9798315793052

Using Multimodal AI to Predict Lung Adenocarcinoma Recurrence /
Ding, Ruiwen,

Using Multimodal AI to Predict Lung Adenocarcinoma Recurrence /Ruiwen Ding. - 1 electronic resource (166 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), is characterized by significant histologic and clinical heterogeneity. Although early-stage NSCLC patients are commonly treated with curative resection, a substantial proportion experience recurrence within five years. Current prognostic tools such as tumor, node, and metastasis staging fail to fully capture tumor aggressiveness for patients with different characteristics. This underscores the need for more accurate, LUAD-specific risk stratification approaches. Recent advances in artificial intelligence (AI) and machine learning (ML) offer new opportunities to derive prognostic biomarkers from routinely acquired data such as radiological scans and histopathology tissue slides. However, existing pathomic models often overlook the spatial context within the tissue slides, and most of them do not explicitly consider different NSCLC subtypes. Furthermore, most AI/ML models rely on unimodal data, missing the complementary insights available from various data modalities. This dissertation addresses these limitations by developing novel AI/ML-based methods to predict cancer aggressiveness in early-stage LUAD patients, using various data modalities such as hematoxylin & eosin-stained whole slide images, preoperative computed tomography scans, and clinical information. The proposed framework captures tumor heterogeneity through spatially-aware pathomic features and integrates different data modalities to improve risk stratification. In doing so, this work aims to improve personalized risk prediction and inform adjuvant therapy decisions by adding additional evidence on top of the standard clinical assessments.

English

ISBN: 9798315793052Subjects--Topical Terms:

657580
Bioengineering.
Subjects--Index Terms:

Digital pathology

Using Multimodal AI to Predict Lung Adenocarcinoma Recurrence /
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520 $a Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), is characterized by significant histologic and clinical heterogeneity. Although early-stage NSCLC patients are commonly treated with curative resection, a substantial proportion experience recurrence within five years. Current prognostic tools such as tumor, node, and metastasis staging fail to fully capture tumor aggressiveness for patients with different characteristics. This underscores the need for more accurate, LUAD-specific risk stratification approaches. Recent advances in artificial intelligence (AI) and machine learning (ML) offer new opportunities to derive prognostic biomarkers from routinely acquired data such as radiological scans and histopathology tissue slides. However, existing pathomic models often overlook the spatial context within the tissue slides, and most of them do not explicitly consider different NSCLC subtypes. Furthermore, most AI/ML models rely on unimodal data, missing the complementary insights available from various data modalities. This dissertation addresses these limitations by developing novel AI/ML-based methods to predict cancer aggressiveness in early-stage LUAD patients, using various data modalities such as hematoxylin & eosin-stained whole slide images, preoperative computed tomography scans, and clinical information. The proposed framework captures tumor heterogeneity through spatially-aware pathomic features and integrates different data modalities to improve risk stratification. In doing so, this work aims to improve personalized risk prediction and inform adjuvant therapy decisions by adding additional evidence on top of the standard clinical assessments.
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590 $a School code: 0031
650 4 $a Bioengineering. $3 657580
650 4 $a Medical imaging. $3 3172799
650 4 $a Oncology. $3 751006
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653 $a Recurrence prediction
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710 2 $a University of California, Los Angeles. $b Bioengineering 0288. $e degree granting institution. $3 3822070
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