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Paper Citation Record · LEDGER

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer

As of 22 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2508.21581.

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

pith.paper-citation-record.v1
2508.21581 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:12:19.240705Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

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  • verified fuzzy0
  • unresolved15
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  • malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0ed7bc5d-0183-494d-8027-141f6ca8f442 · outbound

This paper cites Optuna: A Next-generation Hyperparameter Optimization Framework.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Optuna: A Next-generation Hyperparameter Optimization Framework

Reference 1

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Observation 3385eccf-0760-4a93-ab40-10f308738af4 · outbound

This paper cites https://doi.org/10.7937/K9/TCIA.2016.V6PBVTDR.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer https://doi.org/10.7937/K9/TCIA.2016.V6PBVTDR

Reference 2

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Observation 73ecfe66-77b4-408a-baab-097424ca2d34 · outbound

This paper cites https://doi.org/10.7937/JC8X-9874.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer https://doi.org/10.7937/JC8X-9874

Reference 3

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doi, observed 2026-08-05T14:12:20.253742Z

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Observation 6cae7142-e60f-4e04-963e-f6f66125fbe6 · outbound

This paper cites Uro- logic Oncology: Seminars and Original Investigations 39(7), 438.e11–438.e21 (Jul 2021).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Uro- logic Oncology: Seminars and Original Investigations 39(7), 438.e11–438.e21 (Jul 2021)

Reference 4

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Observation 859b159b-8700-4bd0-bdd8-6281b7865200 · outbound

This paper cites Montorsi, F.: Renal cancer.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Montorsi, F.: Renal cancer

Reference 5

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Observation 94022d0a-57e3-4f86-b069-d7d25594f432 · outbound

This paper cites Med3D: Transfer Learning for 3D Medical Image Analysis.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Med3D: Transfer Learning for 3D Medical Image Analysis

Reference 6

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Observation 42776c49-3bde-4711-b624-fbc3d67f09d1 · outbound

This paper cites Journal of the Royal Statis- tical Society Series B: Statistical Methodology 34(2), 187–202 (Jan 1972).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Journal of the Royal Statis- tical Society Series B: Statistical Methodology 34(2), 187–202 (Jan 1972)

Reference 7

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Observation 52637e93-ad71-4beb-8ac1-c8b999ce2db5 · outbound

This paper cites Journal of Open Source Software 4(40), 1317 (Aug 2019).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Journal of Open Source Software 4(40), 1317 (Aug 2019)

Reference 8

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Observation 5fd9821e-5621-494c-8901-a3d25d0558db · outbound

This paper cites Multimodal Whole Slide Foundation Model for Pathology.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Multimodal Whole Slide Foundation Model for Pathology

Reference 9

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Observation fdd1dc58-d2d2-4be6-81e7-7376a52a3d47 · outbound

This paper cites The Lancet Digital Health 5(8), e515–e524 (Aug 2023).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer The Lancet Digital Health 5(8), e515–e524 (Aug 2023)

Reference 10

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Observation 6c9992fd-546c-4f22-9770-663c95c5967b · outbound

This paper cites McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve

Reference 11

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Observation 246901a9-5f7c-470f-9024-daba45b3efcc · outbound

This paper cites JAMA: The Jour- nal of the American Medical Association 247(18), 2543 (May 1982).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer JAMA: The Jour- nal of the American Medical Association 247(18), 2543 (May 1982)

Reference 12

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Observation 4daebf27-7815-4d2b-b583-7eadb40c3367 · outbound

This paper cites Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 13

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Observation 78e72979-b483-4b69-a7d7-8ee28a7d0b93 · outbound

This paper cites The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Reference 14

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Observation a4732dfe-f0d0-414f-90f4-4d28cac422ad · outbound

This paper cites nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

Reference 15

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Observation 60b3efe8-67a4-4c72-b825-b4af3e5ff0df · outbound

This paper cites Kluger, Y.: Deep- Surv: Personalized treatment recommender system using a Cox proportional haz- ards deep neural network.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Kluger, Y.: Deep- Surv: Personalized treatment recommender system using a Cox proportional haz- ards deep neural network

Reference 16

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Observation 8bbf7857-35aa-4e38-a8c3-3ee91b4eab65 · outbound

This paper cites Cancer 97(7), 1663–1671 (Apr 2003).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Cancer 97(7), 1663–1671 (Apr 2003)

Reference 17

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Observation abad6145-9c5a-431a-ab73-a22f73019616 · outbound

This paper cites European Urology 73(5), 772–780 (May 2018).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer European Urology 73(5), 772–780 (May 2018)

Reference 18

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Observation 7d876b64-883c-4b0b-98be-143b568126fa · outbound

This paper cites Nature Medicine 30(3), 863–874 (Mar 2024).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Nature Medicine 30(3), 863–874 (Mar 2024)

Reference 19

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Observation 377ad755-5927-41ac-b0f8-79ed787c8731 · outbound

This paper cites npj Precision Oncology 8(1), 45 (Feb 2024).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer npj Precision Oncology 8(1), 45 (Feb 2024)

Reference 20

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Observation 45f244d2-7864-474c-b449-e313bb0891c5 · outbound

This paper cites Nature Genetics 45(10), 1113–1120 (Oct 2013).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Nature Genetics 45(10), 1113–1120 (Oct 2013)

Reference 21

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Observation 09df8743-ac04-4fea-b858-3683c3d10161 · outbound

This paper cites Molecular-driven Foundation Model for Oncologic Pathology.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Molecular-driven Foundation Model for Oncologic Pathology

Reference 22

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Observation 79a9f371-797d-4106-95bd-f1ce4527f9b9 · outbound

This paper cites Medical Image Analysis 81, 102559 (Oct 2022).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Medical Image Analysis 81, 102559 (Oct 2022)

Reference 23

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Observation 0a1dbf0b-f7eb-4e85-be86-b9d8b3bbad7b · outbound

This paper cites Nature 634(8035), 970–978 (Oct 2024).

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Nature 634(8035), 970–978 (Oct 2024)

Reference 24

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Observation c1495ba9-678a-4b85-80f5-df0ee698569f · outbound

This paper cites Accelerating Data Processing and Benchmarking of AI Models for Pathology.

Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer Accelerating Data Processing and Benchmarking of AI Models for Pathology

Reference 25

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Pith citing papers

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