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researcher

K. Bera

5 papers hereh-index 336k citations138 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • q-bio.QM4
  • eess.IV1
same name
  • K. Bera — 4 papers, h 4
  • K. Bera — 3 papers, h 5
  • K. Bera — 2 papers, h 17
  • K. Bera — 1 paper, h 11

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202022
most citedNovel Radiomic Measurements of Tumor- Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

41 citations · 56 across the 5 of their papers we have counts for

collaborators
Showing 2020Show all

3 papers · 1 filter

q-bio.QM2020

Spatial-And-Context aware (SpACe) "virtual biopsy" radiogenomic maps to target tumor mutational status on structural MRI

Marwa Ismail, Ramon Correa, Kaustav Bera +8

With growing emphasis on personalized cancer-therapies,radiogenomics has shown promise in identifying target tumor mutational status on routine imaging (i.e. MRI) scans. These appr…

q-bio.QM2020

Can tumor location on pre-treatment MRI predict likelihood of pseudo-progression versus tumor recurrence in Glioblastoma? A feasibility study

Marwa Ismail, Virginia Hill, Volodymyr Statsevych +9

A significant challenge in Glioblastoma (GBM) management is identifying pseudo-progression (PsP), a benign radiation-induced effect, from tumor recurrence, on routine imaging follo…

q-bio.QM2020★ 15 cited

Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study

Nathaniel Braman, Mohammed El Adoui, Manasa Vulchi +10

Predicting response to neoadjuvant therapy is a vexing challenge in breast cancer. In this study, we evaluate the ability of deep learning to predict response to HER2-targeted neo-…

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