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.

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.

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.

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.

Abstract: Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

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.

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.

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.

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.

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.

Abstract: Embodiments facilitate generating a biochemical recurrence (BCR) prognosis by accessing a digitized image of a region of tissue demonstrating prostate cancer (CaP) pathology associated with a patient; generating a set of segmented gland lumen by segmenting a plurality of gland lumen represented in the region of tissue using a deep learning segmentation model; generating a set of post-processed segmented gland lumen; extracting a set of quantitative histomorphometry (QH) features from the digitized image based, at least in part, on the set of post-processed segmented gland lumen; generating a feature vector based on the set of QH features; computing a histotyping risk score based on a weighted sum of the feature vector; generating a classification of the patient as BCR high-risk or BCR low-risk based on the histotyping risk score and a risk score threshold; generating a BCR prognosis based on the classification; and displaying the BCR prognosis.

Abstract: Embodiments access a pre-immunotherapy image of tissue demonstrating NSCLC including a tumor and a peritumoral region; extract a first set of radiomic features from the image; provide the first set of radiomic features to a first machine learning classifier; receive a first probability from the first classifier that the tissue is hyperprogressor (HP) or non-responder (R); if the first probability that the tissue is within a threshold: generate a first classification of the ROT as HP or non-R based on the first probability; if the first probability is not within the threshold: extract a second set of radiomic features from the peritumoral region and provide the second set to a second machine learning classifier; receive a second probability from the second classifier that the tissue is HP or R; generate a second classification of the tissue as HP or R based on the second probability; and display the classification.

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.

Abstract: Embodiments access a pre-immunotherapy image of tissue demonstrating NSCLC including a tumor and a peritumoral region; extract a first set of radiomic features from the image; provide the first set of radiomic features to a first machine learning classifier; receive a first probability from the first classifier that the tissue is hyperprogressor (HP) or non-responder (R); if the first probability that the tissue is within a threshold: generate a first classification of the ROT as HP or non-R based on the first probability; if the first probability is not within the threshold: extract a second set of radiomic features from the peritumoral region and provide the second set to a second machine learning classifier; receive a second probability from the second classifier that the tissue is HP or R; generate a second classification of the tissue as HP or R based on the second probability; and display the classification.

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.

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.