公开(公告)号：US20190259156A1

公开(公告)日：2019-08-22

申请号：US16278325

申请日：2019-02-18

Applicant: Case Western Reserve University

Inventor： Anant Madabhushi ,  Pranjal Vaidya ,  Kaustav Bera ,  Prateek Prasanna ,  Vamsidhar Velcheti

IPC: G06T7/00 ,  G16B40/00 ,  G16B45/00 ,  G16B15/00

Abstract: 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：US11350901B2

公开(公告)日：2022-06-07

申请号：US16200710

申请日：2018-11-27

Applicant: Case Western Reserve University

Inventor： Anant Madabhushi ,  Pranjal Vaidya ,  Vamsidhar Velcheti ,  Kaustav Bera

IPC: A61B6/00 ,  G06T7/00 ,  A61B6/03 ,  G06T7/11 ,  G06T7/12 ,  G06V10/40 ,  G06V10/44 ,  G06V20/69 ,  A61B6/12 ,  G06T7/187 ,  G06T7/136

Abstract: 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：US20210110928A1

公开(公告)日：2021-04-15

申请号：US17065767

申请日：2020-10-08

Applicant: Case Western Reserve University

Inventor： Pranjal Vaidya ,  Kaustav Bera ,  Anant Madabhushi

IPC: G16H50/20 ,  G16B40/30 ,  G06T7/11 ,  G06T7/00 ,  G06K9/46 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G16H30/20 ,  G16H30/40 ,  G16H50/30 ,  G16H50/50 ,  G16H70/60 ,  G16H50/70

Abstract: Embodiments discussed herein facilitate training and/or employing a machine learning model trained on radiomic features, quantitative histomorphometric features, and molecular expression to generate prognoses for treatment of tumors. One example embodiment can access a medical imaging scan of a tumor; segment a peri-tumoral region around the tumor; extract one or more radiomic features from the one or more of the tumor or the peri-tumoral region; provide the one or more radiomic features to a machine learning model trained based on the one or more radiomic features of a training set, one or more quantitative histomorphometric (QH) features of the training set, and a molecular expression of the training set; and receive a prognosis associated with the tumor from the machine learning model.

公开(公告)号：US11610304B2

公开(公告)日：2023-03-21

申请号：US17068089

申请日：2020-10-12

Applicant: Case Western Reserve University

Inventor： Pranjal Vaidya ,  Anant Madabhushi ,  Kaustav Bera

IPC: G06T7/00 ,  G06T7/11 ,  A61B5/00 ,  G06N3/04

Abstract: Embodiments discussed herein facilitate building and/or employing a clinical-radiomics score for determining tumor prognoses based on a combination of a radiomics risk score generated by a machine learning model and clinico-pathological factors. One example embodiment can perform actions comprising: accessing a medical imaging scan of a tumor; segmenting a peri-tumoral region around the tumor; extracting one or more intra-tumoral radiomic features from the tumor and one or more peri-tumoral radiomic features from the peri-tumoral region; providing the one or more intra-tumoral radiomic features and the one or more peri-tumoral radiomic features to a trained machine learning model; receiving a radiomic risk score (RRS) associated with the tumor from the machine learning model; determining a clinical-radiomics score based on the RRS and one or more clinico-pathological factors; and generating a prognosis for the tumor based on the clinical-radiomics 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

Applicant: Case Western Reserve University

Inventor： Anant Madabhushi ,  Pranjal Vaidya ,  Kaustav Bera ,  Prateek Prasanna ,  Vamsidhar Velcheti

IPC: G01N33/48 ,  G06T7/00 ,  G16B15/00 ,  G16B45/00 ,  G16B40/00

Abstract: 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：US20230326582A1

公开(公告)日：2023-10-12

申请号：US18323553

申请日：2023-05-25

Applicant: Case Western Reserve University

Inventor： Pranjal Vaidya ,  Anant Madabhushi ,  Kaustav Bera

IPC: G16H50/20 ,  G06V10/764 ,  G06V10/44 ,  G06F18/214 ,  G06V10/80 ,  G16H30/40 ,  G06T7/00 ,  G06V10/774 ,  G06V10/82

CPC classification number: G16H30/40 ,  G06F18/214 ,  G06T7/0012 ,  G06V10/454 ,  G06V10/764 ,  G06V10/7747 ,  G06V10/811 ,  G06V10/82 ,  G16H50/20 ,  G06T2207/10072 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30061 ,  G06T2207/30096 ,  G06V2201/03

Abstract: The present disclosure, in some embodiments, relates to a method. The method includes using a first machine learning model to generate a first medical prediction associated with a lesion in a medical scan using one or more intra-lesional radiomic features associated with the lesion and the one or more peri-lesional radiomic features associated with a peri-lesional region around the lesion. A second machine learning model is used to generate a second medical prediction associated with the lesion using one or more pathomic features associated with the lesion. A combined medical prediction associated with the lesion is generated using the first medical prediction and the second medical prediction as inputs to a third model.

公开(公告)号：US11464473B2

公开(公告)日：2022-10-11

申请号：US16361667

申请日：2019-03-22

Applicant: Case Western Reserve University

Inventor： Anant Madabhushi ,  Pranjal Vaidya ,  Kaustav Bera

IPC: A61B6/00 ,  G06T7/00 ,  G06K9/62 ,  A61B6/03 ,  G16H30/40 ,  G06N20/00 ,  G06T7/12

Abstract: Embodiments include controlling a processor to access a radiological image of a region of lung tissue, where the radiological image includes a ground glass (GGO) nodule; define a tumoral region by segmenting the GGO nodule, where defining the tumoral region includes defining a tumoral boundary; define a peri-tumoral region based on the tumoral boundary; extract a set of radiomic features from the peri-tumoral region and the tumoral region; provide the set of radiomic features to a machine learning classifier trained to distinguish minimally invasive adenocarcinoma (MIA) and adenocarcinoma in situ (AIS) from invasive adenocarcinoma; receive, from the machine learning classifier, a probability that the GGO nodule is invasive adenocarcinoma, where the machine learning classifier computes the probability based on the set of radiomic features; generate a classification of the GGO nodule as MIA or AIS, or invasive adenocarcinoma, based, at least in part, on the probability; and display the classification.

公开(公告)号：US20210110540A1

公开(公告)日：2021-04-15

申请号：US17068089

申请日：2020-10-12

Applicant: Case Western Reserve University

Inventor： Pranjal Vaidya ,  Anant Madabhushi ,  Kaustav Bera

IPC: G06T7/00 ,  A61B5/00 ,  G06T7/11 ,  G06N3/04

Abstract: Embodiments discussed herein facilitate building and/or employing a clinical-radiomics score for determining tumor prognoses based on a combination of a radiomics risk score generated by a machine learning model and clinico-pathological factors. One example embodiment can perform actions comprising: accessing a medical imaging scan of a tumor; segmenting a peri-tumoral region around the tumor; extracting one or more intra-tumoral radiomic features from the tumor and one or more peri-tumoral radiomic features from the peri-tumoral region; providing the one or more intra-tumoral radiomic features and the one or more peri-tumoral radiomic features to a trained machine learning model; receiving a radiomic risk score (RRS) associated with the tumor from the machine learning model; determining a clinical-radiomics score based on the RRS and one or more clinico-pathological factors; and generating a prognosis for the tumor based on the clinical-radiomics score.

公开(公告)号：US11676703B2

公开(公告)日：2023-06-13

申请号：US17068103

申请日：2020-10-12

Applicant: Case Western Reserve University

Inventor： Pranjal Vaidya ,  Anant Madabhushi ,  Kaustav Bera

IPC: G16H30/40 ,  G06T7/00 ,  G16H50/20 ,  G06F18/214 ,  G06V10/764 ,  G06V10/774 ,  G06V10/80 ,  G06V10/82 ,  G06V10/44

CPC classification number: G16H30/40 ,  G06F18/214 ,  G06T7/0012 ,  G06V10/454 ,  G06V10/764 ,  G06V10/7747 ,  G06V10/811 ,  G06V10/82 ,  G16H50/20 ,  G06T2207/10072 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30061 ,  G06T2207/30096 ,  G06V2201/03

Abstract: Embodiments discussed herein facilitate building and/or employing model(s) for determining tumor prognoses based on a combination of radiomic features and pathomic features. One example embodiment can perform actions comprising: providing, to a first machine learning model, at least one of: one or more intra-tumoral radiomic features associated with a tumor or one or more peri-tumoral radiomic features associated with a peri-tumoral region around the tumor; receiving a first predicted prognosis associated with the tumor from the first machine learning model; providing, to a second machine learning model, one or more pathomic features associated with the tumor; receiving a second predicted prognosis associated with the tumor from the second machine learning model; and generating a combined prognosis associated with the tumor based on the first predicted prognosis and the second predicted prognosis.

公开(公告)号：US20210110541A1

公开(公告)日：2021-04-15

申请号：US17068103

申请日：2020-10-12

Applicant: Case Western Reserve University

Inventor： Pranjal Vaidya ,  Anant Madabhushi ,  Kaustav Bera

IPC: G06T7/00 ,  G16H50/20 ,  G16H30/40 ,  G06K9/62

Abstract: Embodiments discussed herein facilitate building and/or employing model(s) for determining tumor prognoses based on a combination of radiomic features and pathomic features. One example embodiment can perform actions comprising: providing, to a first machine learning model, at least one of: one or more intra-tumoral radiomic features associated with a tumor or one or more peri-tumoral radiomic features associated with a peri-tumoral region around the tumor; receiving a first predicted prognosis associated with the tumor from the first machine learning model; providing, to a second machine learning model, one or more pathomic features associated with the tumor; receiving a second predicted prognosis associated with the tumor from the second machine learning model; and generating a combined prognosis associated with the tumor based on the first predicted prognosis and the second predicted prognosis.