Paper Citation Record · LEDGER

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG

As of 19 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2505.21094.

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

pith.paper-citation-record.v1
2505.21094 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:43:20.337602Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

23 of 23 outbound references displayed

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verified fuzzy5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 916d7bdf-b1e1-4fcc-a086-bd1c00d873ac · outbound

This paper cites In this study, we have used Machine Learning (ML) tools to predict tumor growth and for optimizing treatment regimens.

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG In this study, we have used Machine Learning (ML) tools to predict tumor growth and for optimizing treatment regimens

Reference 1

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This paper cites The exponential growth model, character- ized by a constant growth rate, is frequently used to describe the initial phases of tumor development when resources are abundant.

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG The exponential growth model, character- ized by a constant growth rate, is frequently used to describe the initial phases of tumor development when resources are abundant

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.791061Z

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Observation beea89b1-9d8f-414d-910c-d34df4230bb5 · outbound

This paper cites WHO Report on Cancer: Setting Priorities, In- vesting Wisely and Providing Care for All.

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG WHO Report on Cancer: Setting Priorities, In- vesting Wisely and Providing Care for All

Reference 3

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Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Unresolved cited work

Reference 4

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This paper cites an unresolved cited work.

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Unresolved cited work

Reference 5

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Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Unresolved cited work

Reference 6

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Observation 1ae1e693-9a72-406d-9b49-9dceeecc2116 · outbound

This paper cites K., Pearce, G., Unkundiye P.

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG K., Pearce, G., Unkundiye P

Reference 7

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Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Unresolved cited work

Reference 8

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Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Unresolved cited work

Reference 9

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Observation 7ced8872-59e3-427d-8dde-be04f4c181d5 · outbound

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Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Unresolved cited work

Reference 10

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Source-reported events for the cited work

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Observation 6b56da2e-1dec-499c-9d58-253b5c81dfe5 · outbound

This paper cites et al (2016).

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG et al (2016)

Reference 11

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Observation ba3e1074-e549-4fb6-89f5-86a625990961 · outbound

This paper cites et al (2021).

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG et al (2021)

Reference 12

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Observation 82b5e9f9-af97-4ae8-933e-2d913700d356 · outbound

This paper cites S., Leong, D.

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG S., Leong, D

Reference 13

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Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Unresolved cited work

Reference 14

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Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Unresolved cited work

Reference 15

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Observation bd3c7705-4241-4537-a288-465706ffc32f · outbound

This paper cites et al (2022).

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG et al (2022)

Reference 16

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Observation ac60ec71-d55e-4042-baf4-63a6d1b48c2c · outbound

This paper cites Mathematical modeling in cancer nanomedicine: a review.

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Mathematical modeling in cancer nanomedicine: a review

Reference 17

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Observation 5f5dee8c-3711-4f15-81aa-be717a72a5b2 · outbound

This paper cites et al (2023).

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG et al (2023)

Reference 18

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Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Unresolved cited work

Reference 19

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Observation af8fe8df-2367-4272-8044-7b8fe7bca658 · outbound

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Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG Unresolved cited work

Reference 20

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Observation 7c62a729-a0ff-46df-b940-2b1c18172efe · outbound

This paper cites E., et al (2019).

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG E., et al (2019)

Reference 21

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d9507704-50c0-4d22-a869-9099fdbf085c · outbound

This paper cites V., Clinton, S.

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG V., Clinton, S

Reference 22

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Observation 6402f787-08fc-4c05-a3ad-71faf7eb4150 · outbound

This paper cites et al (2020).

Hybrid Machine Learning and Mathematical Modeling for Tumor Dynamics Prediction: Comparing SPIONs against mNP-FDG et al (2020)

Reference 23

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Source-reported events for the cited work

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Pith citing papers

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