Spatio-Temporal Analysis of Patient-Derived Organoid Videos Using Deep Learning for the Prediction of Drug Efficacy

• URL: http://arxiv.org/abs/2308.14461v1
• Date: Mon, 28 Aug 2023 09:58:34 GMT
• Title: Spatio-Temporal Analysis of Patient-Derived Organoid Videos Using Deep Learning for the Prediction of Drug Efficacy
• Authors: Leo Fillioux, Emilie Gontran, J\'er\^ome Cartry, Jacques RR Mathieu, Sabrina Bedja, Alice Boil\`eve, Paul-Henry Courn\`ede, Fanny Jaulin, Stergios Christodoulidis, Maria Vakalopoulou
• Abstract summary: We propose a novel screening method to assess real-time drug efficacy from a time-lapse microscopy video of PDOs. We report better results than other non-time-resolved methods, indicating that the temporality of data is an important factor for the prediction of ATP.
• Score: 1.3606622227036818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the last ten years, Patient-Derived Organoids (PDOs) emerged as the most reliable technology to generate ex-vivo tumor avatars. PDOs retain the main characteristics of their original tumor, making them a system of choice for pre-clinical and clinical studies. In particular, PDOs are attracting interest in the field of Functional Precision Medicine (FPM), which is based upon an ex-vivo drug test in which living tumor cells (such as PDOs) from a specific patient are exposed to a panel of anti-cancer drugs. Currently, the Adenosine Triphosphate (ATP) based cell viability assay is the gold standard test to assess the sensitivity of PDOs to drugs. The readout is measured at the end of the assay from a global PDO population and therefore does not capture single PDO responses and does not provide time resolution of drug effect. To this end, in this study, we explore for the first time the use of powerful large foundation models for the automatic processing of PDO data. In particular, we propose a novel imaging-based high-throughput screening method to assess real-time drug efficacy from a time-lapse microscopy video of PDOs. The recently proposed SAM algorithm for segmentation and DINOv2 model are adapted in a comprehensive pipeline for processing PDO microscopy frames. Moreover, an attention mechanism is proposed for fusing temporal and spatial features in a multiple instance learning setting to predict ATP. We report better results than other non-time-resolved methods, indicating that the temporality of data is an important factor for the prediction of ATP. Extensive ablations shed light on optimizing the experimental setting and automating the prediction both in real-time and for forecasting.

Related papers

• On the effectiveness of smartphone IMU sensors and Deep Learning in the detection of cardiorespiratory conditions [0.21987601456703473]
  This research introduces an innovative method for the early screening of cardiorespiratory diseases based on an acquisition protocol. We collected, in a clinical setting, a dataset featuring recordings of breathing kinematics obtained by accelerometer and gyroscope readings from five distinct body regions. We propose an end-to-end deep learning pipeline for early cardiorespiratory disease screening, incorporating a preprocessing step segmenting the data into individual breathing cycles.
  arXiv  Detail & Related papers  (2024-08-27T18:29:47Z)
• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• Regressor-free Molecule Generation to Support Drug Response Prediction [83.25894107956735]
  Conditional generation based on the target IC50 score can obtain a more effective sampling space. Regressor-free guidance combines a diffusion model's score estimation with a regression controller model's gradient based on number labels.
  arXiv  Detail & Related papers  (2024-05-23T13:22:17Z)
• Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
  A major challenge for AI-driven drug discovery is the scarcity of high-quality data. We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
  arXiv  Detail & Related papers  (2024-04-16T07:42:55Z)
• Enhancing Acute Kidney Injury Prediction through Integration of Drug Features in Intensive Care Units [0.0]
  The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs has yet to be explored in the critical care setting. This study proposes a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction.
  arXiv  Detail & Related papers  (2024-01-09T05:42:32Z)
• A Transformer-based Prediction Method for Depth of Anesthesia During Target-controlled Infusion of Propofol and Remifentanil [2.960339091215942]
  We propose a transformer-based method for predicting the depth of anesthesia using drug infusions of propofol and remifentanil. Experimental results show that our proposed method outperforms traditional PK-PD models and previous deep learning methods.
  arXiv  Detail & Related papers  (2023-08-02T13:49:44Z)
• Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models [2.140861702387444]
  This paper presents a novel approach to simulating electronic health records using diffusion probabilistic models (DPMs) We demonstrate the effectiveness of DPMs in synthesising longitudinal EHRs that capture mixed-type variables, including numeric, binary, and categorical variables.
  arXiv  Detail & Related papers  (2023-03-22T03:15:33Z)
• DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations [90.27736364704108]
  We present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery. DrugOOD comes with an open-source Python package that fully automates benchmarking processes. We focus on one of the most crucial problems in AIDD: drug target binding affinity prediction.
  arXiv  Detail & Related papers  (2022-01-24T12:32:48Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions [18.242888464394575]
  We propose a novel deep learning framework, namely BridgeDPI. It introduces a class of nodes named hyper-nodes, which bridge different proteins/drugs to work as PPAs and DDAs. In three real-world datasets, we demonstrate that BridgeDPI outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-01-29T12:53:39Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.