Paper Citation Record · LEDGER

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach

As of 8 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2509.06511.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.06511 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:33:09.227559Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T22:29:18.856647Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

12 of 12 outbound references displayed

verified exact2
verified fuzzy2
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation ab0fbdfa-2c46-4934-8a3f-08eca4bdf51a · outbound

This paper cites The Lancet Oncology.20(5), 728–740 (2019).

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach The Lancet Oncology.20(5), 728–740 (2019)

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:08.369338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea341072-4377-49ee-8b05-70adaa40ffb3 · outbound

This paper cites Scientific Data.9(1), 768 (2022).

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach Scientific Data.9(1), 768 (2022)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:08.476758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c28cb00c-c216-41b1-88de-5aadf6594722 · outbound

This paper cites Journal of Clinical Oncology.28(11), 1963–1972 (2010).

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach Journal of Clinical Oncology.28(11), 1963–1972 (2010)

Reference 3

Resolution: verified exact; doi, observed 2026-08-04T23:33:09.328940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3fb5e4ee-85a9-4a87-af04-0b044e07c958 · outbound

This paper cites NeuroImage.54(1), 313–327 (2011).

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach NeuroImage.54(1), 313–327 (2011)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:08.719097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93737024-a14d-4afb-8932-67aedc830f3b · outbound

This paper cites an unresolved cited work.

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach Unresolved cited work

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-04T23:33:09.490747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b7f0de9a-bf79-44c9-96fb-29a3bd947fe5 · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR).

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:33:09.522810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f8d193ac-3f9a-4c2c-81b9-6d455188a13f · outbound

This paper cites In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R.

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:33:09.506292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1ebecd13-6d1b-4665-8dd2-ed3c1a24aec7 · outbound

This paper cites https://doi.org/10.1158/0008-5472.CAN-17-0339.

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach https://doi.org/10.1158/0008-5472.CAN-17-0339

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:09.208685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02602590-29ec-4f99-bd9d-c93e47f2cdde · outbound

This paper cites https://doi.org/10.1038/s42256-023-00652-2.

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach https://doi.org/10.1038/s42256-023-00652-2

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:09.213363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 678a8878-1f39-45d2-a921-3ae48ea49ccd · outbound

This paper cites Human Brain Mapping.

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach Human Brain Mapping

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:09.217887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53491502-9c68-4747-a7f2-853fcfb63e38 · outbound

This paper cites Automated Design of Deep Learning Methods for Biomedical Image Segmentation.

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:09.222215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1048ed81-73e4-4832-8578-3c5550f8cd0b · outbound

This paper cites Neuro-Oncology Advances.5(1), vdad089 (2023).

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach Neuro-Oncology Advances.5(1), vdad089 (2023)

Reference 12

Resolution: verified exact; doi, observed 2026-08-04T23:33:09.265121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 692b3d0a-70a4-4c16-8ad7-a95d51cfd3e1 · inbound

TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment cites this paper.

TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:54:19.976544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 150f46c2-6d4e-4bed-87ff-660fe4b9dc5d · inbound

TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment cites this paper.

TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-03T22:39:00.913252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.