-
公开(公告)号：US10956795B2
公开(公告)日：2021-03-23
申请号：US16111542
申请日：2018-08-24
摘要： Embodiments predict early stage NSCLC recurrence, and include an image acquisition circuit configured to access an image of a region of tissue demonstrating early-stage NSCLC including a plurality of cellular nuclei; a nuclei detecting and segmentation circuit configured to detect a member of the plurality; and classify the member as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; a spatial TIL feature circuit configured to extract spatial TIL features from the plurality, the spatial TIL features including a first subset of features based on the spatial arrangement of TIL nuclei, and a second subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; and an NSCLC recurrence classification circuit configured to compute a probability that region will experience recurrence based on the spatial TIL features; and generate a classification of the region as likely or unlikely to experience recurrence based on the probability.
-
公开(公告)号：US20190259156A1
公开(公告)日：2019-08-22
申请号：US16278325
申请日：2019-02-18
摘要： Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.
-
公开(公告)号：US20190259154A1
公开(公告)日：2019-08-22
申请号：US16281324
申请日：2019-02-21
IPC分类号： G06T7/00 , G06K9/00 , G06K9/46 , G06T7/40 , G06T7/62 , G06K9/62 , G06T7/11 , G16H30/40 , G16H50/20
摘要： Embodiments access a digitized image of tissue demonstrating non-small cell lung cancer (NSCLC), the tissue including a plurality of cellular nuclei; segment the plurality of cellular nuclei represented in the digitized image; extract a set of nuclear radiomic features from the plurality of segmented cellular nuclei; generate at least one nuclear cell graph (CG) based on the plurality of segmented nuclei; compute a set of CG features based on the nuclear CG; provide the set of nuclear radiomic features and the set of CG features to a machine learning classifier; receive, from the machine learning classifier, a probability that the tissue will respond to immunotherapy, based, at least in part, on the set of nuclear radiomic features and the set of CG features; generate a classification of the tissue as a responder or non-responder based on the probability; and display the classification.
-
4.发明申请公开(公告)号：US20180242905A1
公开(公告)日：2018-08-30
申请号：US15883649
申请日：2018-01-30
IPC分类号： A61B5/00 , G16H30/40 , G16H20/40 , G06K9/62 , G06T7/11 , G06T7/00 , G16H50/20 , A61B6/03 , A61B6/00
CPC分类号： A61B5/4839 , A61B5/02007 , A61B5/055 , A61B5/4842 , A61B5/4848 , A61B5/7264 , A61B6/032 , A61B6/504 , A61B6/5217 , G06K9/6262 , G06K9/6268 , G06K2009/00932 , G06K2009/6213 , G06K2209/051 , G06K2209/053 , G06T7/0012 , G06T7/0016 , G06T7/11 , G06T2207/10081 , G06T2207/20081 , G06T2207/30064 , G06T2207/30101 , G06T2207/30172 , G16H20/40 , G16H30/40 , G16H50/20 , G16H50/30 , G16H50/50
摘要： Embodiments classify a region of tissue demonstrating non-small cell lung cancer using quantified vessel tortuosity (QVT). One example apparatus includes annotation circuitry configured to segment a lung region from surrounding anatomy in the region of tissue represented in a radiological image and segment a nodule from the lung region by defining a nodule boundary; vascular segmentation circuitry configured to generate a three dimensional (3D) segmented vasculature by segmenting a vessel associated with the nodule, and to identify a center line of the 3D segmented vasculature; QVT feature extraction circuitry configured to extract a set of QVT features from the radiological image; and classification circuitry configured to compute a probability that the region of tissue will respond to immunotherapy and generate a classification that the region of tissue is a responder or a non-responder based, at least in part, on the probability.
-
公开(公告)号：US11350901B2
公开(公告)日：2022-06-07
申请号：US16200710
申请日：2018-11-27
IPC分类号： A61B6/00 , G06T7/00 , A61B6/03 , G06T7/11 , G06T7/12 , G06V10/40 , G06V10/44 , G06V20/69 , A61B6/12 , G06T7/187 , G06T7/136
摘要： Embodiments generate an early stage NSCLC recurrence prognosis, and predict added benefit of adjuvant chemotherapy. Embodiments include processors configured to access a radiological image of a region of tissue demonstrating early stage NSCLC; segment a tumor represented in the radiological image; define a peritumoral region based on a morphological dilation of a boundary of the tumor; extract a radiomic signature that includes a set of tumoral radiomic features extracted from the tumoral region, and a set of peritumoral radiomic features extracted from the peritumoral region, based on a continuous time to event data; compute a radiomic score based on the radiomic signature; compute a probability of added benefit of adjuvant chemotherapy based on the radiomic score; and generate an NSCLC recurrence prognosis based on the radiomic score. Embodiments may display the radiomic score, or generate a personalized treatment plan based on the radiomic score.
-
公开(公告)号：US10346975B2
公开(公告)日：2019-07-09
申请号：US15613751
申请日：2017-06-05
发明人： Anant Madabhushi , Vamsidhar Velcheti , Mahdi Orooji , Sagar Rakshit , Mehdi Alilou , Niha Beig
摘要： Methods, apparatus, and other embodiments predict tumor infiltrating lymphocyte (TIL) density from pre-surgical computed tomography images of a region of tissue demonstrating non-small cell lung cancer (NSCLC). One example apparatus includes a set of circuits that includes an image acquisition circuit that accesses a radiological image of a region of tissue demonstrating cancerous pathology, where the radiological image has a plurality of pixels, and where the radiological image includes an annotated region of interest (ROI), a feature extraction circuit that extracts a set of radiomic features from the ROI, where the set of radiomic features includes at least two texture features and at least one shape feature, and a classification circuit that comprises a machine learning classifier that classifies the ROI as high tumor infiltrating lymphocyte (TIL) density, or low TIL density, based, at least in part, on the set of radiomic features.
-
公开(公告)号：US20190087693A1
公开(公告)日：2019-03-21
申请号：US16111542
申请日：2018-08-24
摘要： Embodiments predict early stage NSCLC recurrence, and include an image acquisition circuit configured to access an image of a region of tissue demonstrating early-stage NSCLC including a plurality of cellular nuclei; a nuclei detecting and segmentation circuit configured to detect a member of the plurality; and classify the member as a tumor infiltrating lymphocyte (TIL) nucleus or non-TIL nucleus; a spatial TIL feature circuit configured to extract spatial TIL features from the plurality, the spatial TIL features including a first subset of features based on the spatial arrangement of TIL nuclei, and a second subset of features based on the spatial relationship between TIL nuclei and non-TIL nuclei; and an NSCLC recurrence classification circuit configured to compute a probability that region will experience recurrence based on the spatial TIL features; and generate a classification of the region as likely or unlikely to experience recurrence based on the probability.
-
公开(公告)号：US10078895B2
公开(公告)日：2018-09-18
申请号：US15389872
申请日：2016-12-23
CPC分类号： G06T7/0012 , G06K9/00147 , G06K9/3233 , G06K9/4604 , G06K9/4628 , G06K9/6267 , G06K9/6269 , G06K9/6271 , G06K9/6277 , G06K9/6281 , G06K9/6286 , G06K9/6287 , G06K9/66 , G06N3/0472 , G06N3/08 , G06N20/00 , G06T7/11 , G06T2207/10004 , G06T2207/10024 , G06T2207/20036 , G06T2207/20081 , G06T2207/30024 , G06T2207/30061 , G06T2207/30096 , G16H50/20
摘要： Methods and apparatus predict non-small cell lung cancer (NSCLC) recurrence using radiomic features extracted from digitized hematoxylin and eosin (H&E) stained slides of a region of tissue demonstrating NSCLC. One example apparatus includes an image acquisition circuit that acquires an image of a region of tissue demonstrating NSCLC, a segmentation circuit that segments a cellular nucleus from the image, a feature extraction circuit that extracts a set of features from the image, a tumor infiltrating lymphocyte (TIL) identification circuit that classifies the segmented nucleus as a TIL or non-TIL, a graphing circuit that constructs a TIL graph and computes a set of TIL graph statistical features, and a classification circuit that computes a probability that the region will experience NSCLC recurrence. The classification circuit may compute a quantitative continuous image-based risk score based on the probability or the image. A treatment plan may be provided based on the risk score.
-
公开(公告)号：US11574404B2
公开(公告)日：2023-02-07
申请号：US16278325
申请日：2019-02-18
摘要： Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.
-
公开(公告)号：US10441225B2
公开(公告)日：2019-10-15
申请号：US16236675
申请日：2018-12-31
摘要： Embodiments include operations, apparatus, methods and other embodiments that access a baseline CT image of a region of tissue (ROT) demonstrating non-small cell lung cancer (NSCLC), segment a tumoral region represented in the baseline CT image; define a peritumoral region by dilating the tumoral boundary; extract a set of tumoral radiomic features from the tumoral region, a set of peritumoral radiomic features from the peritumoral region, and a set of clinico-pathologic features from the baseline CT image; provide the set of tumoral radiomic features, peritumoral radiomic features, and clinico-pathologic features to a machine learning classifier; receive, from the machine learning classifier, a time-to-recurrence post trimodality therapy (TMT) prediction, based on the set of tumoral radiomic features, peritumoral radiomic features, and clinico-pathologic features; generate a classification of the ROT as an MPR responder or MPR non-responder based, at least in part, on the time-to-recurrence post-TMT prediction; and display the classification.
-
-
-
-
-
-
-
-
-
搜索结果
28 条记录 (耗时 0.021 秒)
