Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED OFFICE ACTION
Status of Claims
Claims 33-47 are pending examination.
Claims 1-32 are cancelled
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b) (2) (C) for any potential 35 U.S.C. 102(a) (2) prior art against the later invention.
1. Claims 33,34,35 and 47 are rejected under 35 U.S.C 103 as being patentable over Mekkaoui ( USPUB 20160061920) in view of Lina Yu ( NPL Doc: "iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images," 2017 IEEE International Conference on Big Data, Pages 3916-3922.) .
As per claim 33, Mekkaoui teaches A method of characterising epicardial or myocardial tissue biology in a region of interest comprising epicardial or myocardial tissue of a subject ( Paragraph [0010]- “…a map that is representative of myocardial tissue architecture for locations within a region of interest in the subject's heart can be generated….” AND Paragraph [0028]- “…circumferential and longitudinal axis for each location. In particular, the at least one processor 104 may be configured to compute a Euclidean distance map with respect to any target locations or points of reference, such as an epicardial surface,…”) , the method comprising:(a) obtaining or having obtained medical imaging data of the region of interest of the subject ( Paragraphs [0028-0029]- “… the software 110 may contain instructions directed to producing local FAMs for locations, or voxels, within a region of interest, as mentioned. The data 112 may take include any data necessary for operating the system 100, and may include any raw or processed information in relation to anatomical data, diffusion data, and so forth. In addition, the output 112 may take any shape or form, …”) ;
Mekkaoui does not explicitly teach (b) extracting a radiomic feature from the medical imaging data; and (c) calculating a value of a radiomic signature of the region of interest using the obtained medical imaging data; wherein the radiomic signature is calculated on the basis of the radiomic feature extracted from the medical imaging data.
However, within analogous art, Lina Yu teaches (b) extracting a radiomic feature from the medical imaging data ( Page 3918- Fig. 1- “Fig. 1. An overview of the key steps of our visual analytics workflow using medical images, extracted radiomic features, and clinical features. (a) Non-image features are collected from clinical data. (b) Experienced physicians contour tumor areas on 2D CT slices, given two examples cases (A and B) in lung cancer patients. 3D volume (c) and tumor objects (d) are extracted according to 2D contours. (e) Radiomic features are extracted from the reconstructed tumors,…” AND Page 3917- Col. 2- “…B. Radiomic Features We adopt and extend the radiomic features defined in the existing work [5] to describe tumor phenotype characteristics. With our application and other potential domains in mind, we use 94 features as shown in Table I and group these features into the following three categories:• First order statistics. This group of features describes the distribution of tumor image intensities. It consists of the computation of energy, entropy, kurtosis, skewness, and so on. These features help us know the histogram dispersion, asymmetry, and sharpness of tumor intensities….”); and (c) calculating a value of a radiomic signature of the region of interest using the obtained medical imaging data ( Page 3921- Col.2- “…identifying the association of radiomic expression patterns with tumor stage and survival time is essential for capturing prognostic radiomic signatures and developing predictive models of survival. The correlation matrix in our system, displaying the correlation coefficient of each pair of features, can facilitate the analyst to first quickly grasp an overview of the relationship among the features and then examine the details. Figure 6 shows the correlation matrix of the clinical features and the radiomic features. The clinical features include age, gender,T-stage, N-stage, Overall stage, and Survival time. From the correlation matrix, we can clearly see that there are strong correlations among the most shape and size features. The only exceptions are Elongation and Flatness, which are strongly correlated with each other but no other features. These two features also do not convey a significant correlation with Survival time. Therefore, the analyst can possibly neglect them when selecting radiomic signatures….”); wherein the radiomic signature is calculated on the basis of the radiomic feature extracted from the medical imaging data ( Page 3923- Col. 1 – “…in Figure 5, the radiomic features of the patients #77 and #99 exhibit a significant similarity, and are tightly coupled to one cluster. However, the survival times of these two patients are 58 days and 493 days, respectively, showing a less optimal performance of the derived radiomic features. By closely examining their 3D structures (Figure 9), we observe that these two tumors have different global structural characteristics (e.g., location), although their local characteristics are very similar. We would like to investigate new global information based approaches [19] to improving the prognostic performance of radiomic signatures….”) .
One of ordinary skill in the art would have been motivated to combine the teaching of Lina Yu within the modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui because the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu provides a system and method for implementing radiomic analysis of features quantifying medical image intensity , shape and texture from plurality of medical images of a region of interest.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu within the modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui for implementation of a system and method for radiomic analysis of features quantifying medical image intensity , shape and texture from plurality of medical images of a region of interest.
As per claim 34, Combination of Mekkaoui and Lina Yu teach claim 33,
Mekkaoui teaches wherein the region of interest is an epicardial region ( Paragraph [0028]- “…the at least one processor 104 may be configured to compute a Euclidean distance map with respect to any target locations or points of reference, such as an epicardial surface,…”) .
As per claim 35, Combination of Mekkaoui and Lina Yu teach claim 33,
Within analogous art, Lina Yu teaches wherein the value of the radiomic signature is used to characterize cardiac health or disease of the subject ( Page 3921- Col. 2- “…After gaining the patterns of the features, the analyst can choose the features in the clusters and further explore the relationship between the features. In particular, identifying the association of radiomic expression patterns with tumor stage and survival time is essential for capturing prognostic radiomic signatures and developing predictive models of survival….”) .
As per claim 47, Combination of Mekkaoui and Lina Yu teach claim 33,
Mekka teaches wherein the radiomic signature is calculated on the basis of two or more radiomic features extracted from the medical imaging data( Page 3921- Col.2- “…identifying the association of radiomic expression patterns with tumor stage and survival time is essential for capturing prognostic radiomic signatures and developing predictive models of survival. The correlation matrix in our system, displaying the correlation coefficient of each pair of features, can facilitate the analyst to first quickly grasp an overview of the relationship among the features and then examine the details. Figure 6 shows the correlation matrix of the clinical features and the radiomic features. The clinical features include age, gender,T-stage, N-stage, Overall stage, and Survival time. From the correlation matrix, we can clearly see that there are strong correlations among the most shape and size features. The only exceptions are Elongation and Flatness, which are strongly correlated with each other but no other features. These two features also do not convey a significant correlation with Survival time. Therefore, the analyst can possibly neglect them when selecting radiomic signatures….”).
2. Claims 36 and 42 are rejected under 35 U.S.C 103 as being patentable over Mekkaoui ( USPUB 20160061920) in view of Lina Yu ( NPL Doc: "iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images," 2017 IEEE International Conference on Big Data, Pages 3916-3922.) in further view of E.O Rodrigues et al. ( NPL Doc: " Towards the automated segmentation of epicardial and mediastinal fats: A multi-manufacturer approach using intersubject registration and random forest,"18th June 2015, 2015 IEEE International Conference on Industrial Technology (ICIT) ,Pages 1779-1784.) .
As per claim 36, Combination of Mekkaoui and Lina Yu teach claim 35,
Combination of Mekkaoui and Lina Yu does not explicitly teach wherein the cardiac health or disease is selected from the group consisting of myocardial fibrosis, myocardial redox state, myocardial inflammation, and myocardial gene expression patterns.
Within analogous art , E.O Rodrigues et al. teaches wherein the cardiac health or disease is selected from the group consisting of myocardial fibrosis, myocardial redox state, myocardial inflammation, and myocardial gene expression patterns ( Abstract – “…the cardiac fat varies unrelated to the overall fat of the subject, and, therefore, it reinforces the quantitative analysis of these adipose tissues as being essential. Clinical decision support systems are computer programs capable of evaluating information and providing a corresponding diagnosis or data to complement the physicists’ analyses. …fully automatically segmenting two types of cardiac adipose tissues that stand apart from each other by the pericardium on CT images obtained by the standard acquisition protocol used for coronary calcium scoring….” AND Page 1779- Col. 2- “…Several studies also correlate other cardiovascular risk factors and outcomes to the epicardial adipose tissue volume such as myocardial infarction [13], atrial fibrillation and ablation outcome [12],carotid stiffness [14], atherosclerosis [8,9], and many others…”) .
One of ordinary skill in the art would have been motivated to combine the teaching of E.O Rodrigues et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu because the Towards the automated segmentation of epicardial and mediastinal fats: A multi-manufacturer approach using intersubject registration and random forest mentioned by E.O Rodrigues et al. provides a system and method for implementing extraction of features related to pixels and their surrounding area and a segmentation step based on data mining classification algorithms.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Towards the automated segmentation of epicardial and mediastinal fats: A multi-manufacturer approach using intersubject registration and random forest mentioned by E.O Rodrigues et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu for implementation of a system and method for extraction of features related to pixels and their surrounding area and a segmentation step based on data mining classification algorithms.
As per claim 42, Combination of Mekkaoui and Lina Yu teach claim 33,
Combination of Mekkaoui and Lina Yu does not explicitly teach wherein the radiomic signature is indicative of one or more of the following biological phenotypes of the myocardium or epicardium: oxidative stress, redox state, inflammation or fibrosis.
Within analogous art , E.O Rodrigues et al. teaches wherein the radiomic signature is indicative of one or more of the following biological phenotypes of the myocardium or epicardium: oxidative stress, redox state, inflammation or fibrosis ( Abstract – “…the cardiac fat varies unrelated to the overall fat of the subject, and, therefore, it reinforces the quantitative analysis of these adipose tissues as being essential. Clinical decision support systems are computer programs capable of evaluating information and providing a corresponding diagnosis or data to complement the physicists’ analyses. …fully automatically segmenting two types of cardiac adipose tissues that stand apart from each other by the pericardium on CT images obtained by the standard acquisition protocol used for coronary calcium scoring….” AND Page 1779- Col. 2- “…Several studies also correlate other cardiovascular risk factors and outcomes to the epicardial adipose tissue volume such as myocardial infarction [13], atrial fibrillation and ablation outcome [12],carotid stiffness [14], atherosclerosis [8,9], and many others…”) .
One of ordinary skill in the art would have been motivated to combine the teaching of E.O Rodrigues et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu because the Towards the automated segmentation of epicardial and mediastinal fats: A multi-manufacturer approach using intersubject registration and random forest mentioned by E.O Rodrigues et al. provides a system and method for implementing extraction of features related to pixels and their surrounding area and a segmentation step based on data mining classification algorithms.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Towards the automated segmentation of epicardial and mediastinal fats: A multi-manufacturer approach using intersubject registration and random forest mentioned by E.O Rodrigues et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu for implementation of a system and method for extraction of features related to pixels and their surrounding area and a segmentation step based on data mining classification algorithms.
3. Claims 36 and 42 are rejected under 35 U.S.C 103 as being patentable over Mekkaoui ( USPUB 20160061920) in view of Lina Yu ( NPL Doc: "iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images," 2017 IEEE International Conference on Big Data, Pages 3916-3922.) in further view of Lure et al. ( USPUB 20170337681) .
As per claim 38, Combination of Mekkaoui and Lina Yu teach claim 33,
Combination of Mekkaoui and Lina Yu does not explicitly teach wherein the radiomic signature is used as a risk stratification tool for determining the development of heart disease in the subject.
Within analogous art, Lure et al. teaches wherein the radiomic signature is used as a risk stratification tool for determining the development of heart disease in the subject ( Paragraphs [0054-0056]- “…thresholds determination of cardiac-based features 00210-3520-001-01, thresholds determination of boundary-based radiomics (thickness, completeness, etc.) 00210-3520-001-02, and thresholds determination of diaphragm related features (00210-3520-001-03). These thresholds are sent to lung boundary-based radiomics classifiers 00210-3520-001-04 for classification….”) .
One of ordinary skill in the art would have been motivated to combine the teaching of Lure et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu because the System And Method For The Classification Of Healthiness Index From Chest Radiographs Of A Healthy Person mentioned by Lure et al. provides a system and method for implementing classification of different healthiness indices, using radiomics and neural network algorithm.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the System And Method For The Classification Of Healthiness Index From Chest Radiographs Of A Healthy Person mentioned by Lure et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu for implementation of a system and method for classification of different healthiness indices, using radiomics and neural network algorithm.
4. Claims 40 and 41 are rejected under 35 U.S.C 103 as being patentable over Mekkaoui ( USPUB 20160061920) in view of Lina Yu ( NPL Doc: "iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images," 2017 IEEE International Conference on Big Data, Pages 3916-3922.) in further view of Evangelos K. Oikonomou et al. ( NPL Doc. : “Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of
cardiovascular disease,” 24th February 2020 ( Revised 19th August 2019) , Artificial intelligence in cardiac CT Cardiovascular Research (2020) 116,Pages 2040–2054. ).
As per claim 40, Combination of Mekkaoui and Lina Yu teach claim 33,
Combination of Mekkaoui and Lina Yu does not explicitly teach wherein the radiomic signature is used to determine a risk of the subject developing arrhythmia.
Within analogous art, Evangelos K. Oikonomou teaches wherein the radiomic signature is used to determine a risk of the subject developing arrhythmia ( Page 2044- Col. 2- “…DNNs have been shown to have high sensitivity (93%) and specificity (90%) in diagnosing acute myocardial infarction,32 as well as classifying arrhythmias and electrical conduction abnormalities, with accuracy comparable with that of trained cardiologists…” AND Page 2050- Table 1 – “…Where risk groups are to be defined based on a radiomic signature, the method for cut-off identification should be defined a priori…” ) .
One of ordinary skill in the art would have been motivated to combine the teaching of Evangelos K. Oikonomou et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu because the Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease mentioned by Evangelos K. Oikonomou et al. provides a system and method for implementing the detection of precise phenotyping of cardiovascular disease.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease mentioned by Evangelos K. Oikonomou et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu for implementation of a system and method for the detection of precise phenotyping of cardiovascular disease.
As per claim 41, Combination of Mekkaoui and Lina Yu teach claim 33,
Combination of Mekkaoui and Lina Yu does not explicitly teach wherein the radiomic signature is used to evaluate the likelihood of atrial fibrillation (AF) occurring in the subject.
Within analogous art, Evangelos K. Oikonomou et al. teaches wherein the radiomic signature is used to evaluate the likelihood of atrial fibrillation (AF) occurring in the subject ( Page 2041- Graphical Abstract- Radiomics AND Page 2044- Col. 2- “…The power of big data and AI was demonstrated in a recent landmark study which analysed 180 922 patients with 649 931 normal sinus rhythm ECGs and demonstrated that a CNN algorithm was able to reliably detect the presence of atrial fibrillation [AUC of 0.87 (95% confidence interval 0.86–0.88)].34 More recently, however, with the increasing adoption of cardiac CT as the go-to test for the non-invasive assessment of CAD,11 the focus of AI research has expanded to the
analysis and interpretation of cardiac CT scans….”) .
One of ordinary skill in the art would have been motivated to combine the teaching of Evangelos K. Oikonomou et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu because the Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease mentioned by Evangelos K. Oikonomou et al. provides a system and method for implementing the detection of precise phenotyping of cardiovascular disease.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease mentioned by Evangelos K. Oikonomou et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu for implementation of a system and method for the detection of precise phenotyping of cardiovascular disease.
5. Claims 43,44 and 46 are rejected under 35 U.S.C 103 as being patentable over Mekkaoui ( USPUB 20160061920) in view of Lina Yu ( NPL Doc: "iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images," 2017 IEEE International Conference on Big Data, Pages 3916-3922.) in further view of KULLBERG et al. ( USPUB 20180144472) .
As per claim 43, Combination of Mekkaoui and Lina Yu teach claim 33,
Combination of Mekkaoui and Lina Yu does not explicitly teach wherein the radiomic signature is used to determine the presence of a myocardial disease or a heart condition associated with a myocardial disease in the subject.
Within analogous art, KULLBERG et al. teaches wherein the radiomic signature is used to determine the presence of a myocardial disease or a heart condition associated with a myocardial disease in the subject ( Paragraph [0006]- “In radiomics, a feature analysis of pre-segmented regions can be made (Ref. 2). This is an initiative to use radiology medical imaging to monitor the development and progression of cancer or its response to therapy providing a comprehensive quantification of a tumor phenotype. Radiomics enables high-throughput extraction of a large amount of quantitative features from radiology medical images of a given modality, such as computed tomography (CT), positron emission tomography (PET), and MR, and can provide complementary and interchangeable information compared to sources such as demographics, pathology, blood biomarkers, or genomics, improving individualized treatment selection and monitoring. The statistical analysis is radiomics…” AND Myocardial taught within Paragraph [0159]- “…“prediction-maps” 941 of what whole-body tissue features predict “time to event” or the future risk of, for instance, type-2 diabetes, myocardial infarction, stroke or dementia, respectively….”) .
One of ordinary skill in the art would have been motivated to combine the teaching of KULLBERG et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu because the Whole body image registration method and method for analyzing images thereof-mentioned by KULLBERG et al. provides a system and method for implementing image registration of human body for analysis of disease with the human body.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Whole body image registration method and method for analyzing images thereof-mentioned by KULLBERG et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu for implementation of a system and method for image registration of human body for analysis of disease with the human body.
As per claim 44, Combination of Mekkaoui and Lina Yu teach claim 33,
Combination of Mekkaoui and Lina Yu does not explicitly teach wherein the radiomic signature is used to predict or categorise the risk of stroke in the subject.
Within analogous art, KULLBERG et al. teaches wherein the radiomic signature is used to predict or categorise the risk of stroke in the subject( Paragraph [0006]- “In radiomics, a feature analysis of pre-segmented regions can be made (Ref. 2). This is an initiative to use radiology medical imaging to monitor the development and progression of cancer or its response to therapy providing a comprehensive quantification of a tumor phenotype. Radiomics enables high-throughput extraction of a large amount of quantitative features from radiology medical images of a given modality, such as computed tomography (CT), positron emission tomography (PET), and MR, and can provide complementary and interchangeable information compared to sources such as demographics, pathology, blood biomarkers, or genomics, improving individualized treatment selection and monitoring. The statistical analysis is radiomics…” AND Myocardial taught within Paragraph [0159]- “…“prediction-maps” 941 of what whole-body tissue features predict “time to event” or the future risk of, for instance, type-2 diabetes, myocardial infarction, stroke or dementia, respectively….”).
One of ordinary skill in the art would have been motivated to combine the teaching of KULLBERG et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu because the Whole body image registration method and method for analyzing images thereof-mentioned by KULLBERG et al. provides a system and method for implementing image registration of human body for analysis of disease with the human body.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Whole body image registration method and method for analyzing images thereof-mentioned by KULLBERG et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu for implementation of a system and method for image registration of human body for analysis of disease with the human body.
As per claim 46, Combination of Mekkaoui and Lina Yu teach claim 33,
Combination of Mekkaoui and Lina Yu does not explicitly teach wherein the radiomic signature is used to determine or detect high atrial fibrosis activity and/or active inflammation in the subject .
Within analogous art, KULLBERG et al. teaches wherein the radiomic signature is used to determine or detect high atrial fibrosis activity and/or active inflammation in the subject ( Paragraph [0006]- “In radiomics, a feature analysis of pre-segmented regions can be made (Ref. 2). This is an initiative to use radiology medical imaging to monitor the development and progression of cancer or its response to therapy providing a comprehensive quantification of a tumor phenotype. Radiomics enables high-throughput extraction of a large amount of quantitative features from radiology medical images of a given modality, such as computed tomography (CT), positron emission tomography (PET), and MR, and can provide complementary and interchangeable information compared to sources such as demographics, pathology, blood biomarkers, or genomics, improving individualized treatment selection and monitoring. The statistical analysis is radiomics…” AND Paragraphs [0080]- “…Multiple local feature/texture measures, such as intensity heterogeneity may also be measured, which can be used to determine deviations of tissue heterogeneity, for example due to pathologies such as tumors or fibrosis….”) .
One of ordinary skill in the art would have been motivated to combine the teaching of KULLBERG et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu because the Whole body image registration method and method for analyzing images thereof-mentioned by KULLBERG et al. provides a system and method for implementing image registration of human body for analysis of disease with the human body.
Therefore, it would have been obvious for one in the ordinary skills in the art before the effective filing date of the claimed invention to implement the Whole body image registration method and method for analyzing images thereof-mentioned by KULLBERG et al. within the combined modified teaching of the Mapping cardiac tissue architecture systems and methods mentioned by Mekkaoui and the iVAR: Interactive Visual Analytics of Radiomics Features from Large-Scale Medical Images mentioned Lina Yu for implementation of a system and method for image registration of human body for analysis of disease with the human body.
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Allowable Subject Matter
6. Claims 37 , 39 and 45 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
7. The following is an examiner’s statement of reasons for objecting the claims as allowable subject matter:
As to claim 37, prior art of record does not teach or suggest the limitation mentioned within claim 37: “ the radiomic signature is indicative of gene expression profiles that can be used as surrogate biomarkers of the epicardial or myocardial tissue biology of the subject. ”
As to claim 39, prior art of record does not teach or suggest the limitation mentioned within claim 39: “ the radiomic signature may be is used on its own or combined with non-imaging patient risk factors and/or demographic features and/or existing models to provide diagnostic or prognostic information relating to epicardial or myocardial biology of the subject.”
As to claim 45, prior art of record does not teach or suggest the limitation mentioned within claim 45: “ the radiomic signature is used to predict the risk of the subject developing, or to determine whether the subject has, one or more of heart arrhythmia, ischaemic heart disease, heart failure, or cardiomyopathy.”
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Examiner’s Notes
8. The Examiner acknowledges the following prior arts below as pertinent to the current applications claim limitations and inventive concept, although the following prior arts shown below were not relied upon to address the limitations within the claim , they are analogous art mentioning the inventive concept key points on ( Medical image processing , radiomic imaging , features, radiomic signature, cardiac health assessment , neural network etc.).
1) Mohammad Edalat-Javid et al.," Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study,” 18th November 2019, Journal of Nuclear Cardiology Volume 28, Number 6, Pages 2730-2741.
2) Chun-Qiang Lu et al.," Diabetes risk assessment with imaging: a radiomics studyof abdominal CT," 11th June 2018, European Radiology (2019) 29,Pages 2234-2240.
3) Rong Yuan et al.," Radiomics in RayPlus: a Web-Based Tool for Texture Analysis in Medical Images," 22nd October 2018, Journal of Digital Imaging (2019) 32 ,Pages 270-273.
4) Frederic Commandeur et al.," Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT," 15th December 2017, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 37, NO. 8, AUGUST 2018, Pages 1835-1844.
5) Nikolaos Alexopoulos et al. ,”Effect of Intensive Versus Moderate Lipid-Lowering Therapy on Epicardial Adipose Tissue in Hyperlipidemic Post-Menopausal Women,”16th December 2012, Journal of the American College of Cardiology Vol. 61, No. 19, 2013,Pages 1957-1960.
6) Michael T. Lu et al.," Epicardial and paracardial adipose tissue volume and attenuation Association with high-risk coronary plaque on computed tomographic angiography in the ROMICAT II trial," 20th May 2016, Atherosclerosis 251 (2016), Pages 47-51.
7) Klara J. Rosenquist et al. ," Visceral and Subcutaneous Fat Quality and Cardiometabolic Risk," 9th November 2012, JACC : CARDIOVASCULAR IMAGING, VOL .6 , NO . 7 , 2013, Pages 762-767.
8) Márton Kolossváry et al., " Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic Metrics to Identify Coronary Plaques With Napkin-Ring Sign," 19th October 2017,Circulation: Cardiovascular Imaging, Volume 10, Number 12, Pages 1-9.
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Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892, Notice of Reference Cited for a listing of analogous art.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to OMAR S ISMAIL whose telephone number is (571)272-9799 and Fax # is (571)273-9799. The examiner can normally be reached on M-F 9:00am-6:00pm.
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/OMAR S ISMAIL/
Primary Examiner, Art Unit 2635
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