What is he best known for?
The fields of study he is best known for:
- Cancer
- Internal medicine
- Surgery
His primary areas of investigation include Radiology, Nuclear medicine, Breast cancer, Dual time point and 18f fdg pet.
The study of Radiology is intertwined with the study of Osteomyelitis in a number of ways.
The various areas that Rakesh Kumar examines in his Nuclear medicine study include Lung cancer and Metastasis.
Rakesh Kumar combines subjects such as Pathology and Histopathology with his study of Breast cancer.
His Pathology study integrates concerns from other disciplines, such as Neoplasm staging, False Negative Reactions, Oncology and Primary breast cancer.
The study incorporates disciplines such as Lesion, Biopsy, Retrospective cohort study and Primary cancer in addition to 18f fdg pet.
His most cited work include:
- Potential of dual-time-point imaging to improve breast cancer diagnosis with 18F-FDG PET (193 citations)
- 18F-FDG PET in Evaluation of Adrenal Lesions in Patients with Lung Cancer (183 citations)
- Clinicopathologic factors associated with false negative FDG-PET in primary breast cancer. (148 citations)
What are the main themes of his work throughout his whole career to date?
Rakesh Kumar mainly focuses on Radiology, Nuclear medicine, Positron emission tomography, Pathology and Biopsy.
His study in Radiology is interdisciplinary in nature, drawing from both Metastasis and Lymph node.
His Nuclear medicine study combines topics from a wide range of disciplines, such as Skeletal disease, Malignancy, Adult patients and Surgical interventions.
His studies deal with areas such as Breast cancer, Neoplasm staging, Oncology and Internal medicine as well as Pathology.
The Primary breast cancer, CA15-3 and Sentinel lymph node research he does as part of his general Breast cancer study is frequently linked to other disciplines of science, such as Extramural, therefore creating a link between diverse domains of science.
Rakesh Kumar has researched Biopsy in several fields, including Stage and Lymphoma.
He most often published in these fields:
- Radiology (58.18%)
- Nuclear medicine (23.64%)
- Positron emission tomography (18.18%)
What were the highlights of his more recent work (between 2010-2020)?
- Architecture (3.64%)
- Artificial intelligence (3.64%)
- Word (3.64%)
In recent papers he was focusing on the following fields of study:
Architecture, Artificial intelligence, Word, Natural language processing and Cloud computing are his primary areas of study.
His research in the fields of Machine-readable dictionary, Intelligent decision support system, Natural language and Phrase overlaps with other disciplines such as Meaning.
His SemEval and Word-sense disambiguation study in the realm of Word connects with subjects such as Set and Meaning.
His study in the field of Information extraction and Machine translation is also linked to topics like Key.
His Cloud computing research is multidisciplinary, incorporating elements of Software engineering and Database.
He combines subjects such as Virtualization and Dashboard with his study of Software.
Between 2010 and 2020, his most popular works were:
- Gallium-68-DOTA-NOC PET/CT of patients with gastroenteropancreatic neuroendocrine tumors: a prospective single-center study. (88 citations)
- Role of MRI in osteosarcoma for evaluation and prediction of chemotherapy response: correlation with histological necrosis (69 citations)
- A Review of Literature on Word Sense Disambiguation (10 citations)
In his most recent research, the most cited papers focused on:
- Cancer
- Internal medicine
- Surgery
His main research concerns Radiology, Standardized uptake value, Key, Artificial intelligence and Information extraction.
His work carried out in the field of Radiology brings together such families of science as Necrosis, Prospective cohort study, Predictive value of tests and Pathology.
His work deals with themes such as PET-CT, Primary tumor, Single Center and Histopathology, which intersect with Standardized uptake value.
His Histopathology research incorporates themes from Neuroendocrine tumors and Nuclear medicine.
Key is connected with Meaning, Word, Word-sense disambiguation, Natural language processing and SemEval in his research.
His work on Answering Machine expands to the thematically related Artificial intelligence.
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