Pith Number

pith:3PNIV66F

pith:2026:3PNIV66FWSLFHJBQZ6UF7D2XGA

not attested not anchored not stored refs resolved

Prediction of Rectal Cancer Regrowth from Longitudinal Endoscopy

Aneesh Rangnekar, Christina Lee, Despoina Kanata, Francisco Sanchez-Vega, Hannah Thompson, Hannah Williams, Harini Veeraraghavan, J. Joshua Smith, Jorge Tapias Gomez, Julio Garcia-Aguilar, Mert R. Sabuncu

A longitudinal deep learning model detects rectal cancer regrowth from paired endoscopy images with 97 percent sensitivity.

arxiv:2605.12855 v1 · 2026-05-13 · cs.CV

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\usepackage{pith}
\pithnumber{3PNIV66FWSLFHJBQZ6UF7D2XGA}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

TREX achieved the highest accuracy in detecting LR with a high sensitivity of 97% ± 6% and a balanced accuracy of 90% ± 3%, and outperformed all baselines in early detection at both 3--6 (74% ± 1%) and 6--12 months (62% ± 4%) prior to clinical detection.

C2weakest assumption

The clinical trial dataset used for training and testing is representative of broader patient populations and imaging conditions, and that performance on held-out data will translate to prospective real-world use without significant domain shift.

C3one line summary

TREX detects rectal cancer local regrowth from longitudinal endoscopy image pairs with 97% sensitivity and enables early prediction 3-12 months before clinical confirmation, outperforming baselines.

References

55 extracted · 55 resolved · 1 Pith anchors

[1] Diseases of the Colon and Rectum 67(1), 18–31 (2024)https://doi.org/10.1097/DCR 2023 · doi:10.1097/dcr

[2] Journal of Clinical Oncology40(23), 2546–2556 (2022)https://doi.org/10.1200/JCO.22.00032 https://ascopubs.org/doi/pdf/10.1200/JCO.22.00032 2022 · doi:10.1200/jco.22.00032

[3] Journal of Clinical Oncology42(5), 500–506 (2024)https://doi.org/10.1200/JCO.23.01208 https://ascopubs.org/doi/pdf/10.1200/JCO.23.01208 2024 · doi:10.1200/jco.23.01208

[4] An- nals of surgery268(6), 955–967 (2018) 2018

[5] The Lancet Gastroenterology & Hep- atology3(12), 825–836 (2018)https://doi.org/10 2018

Receipt and verification

First computed	2026-05-18T03:09:11.799391Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

dbda8afbc5b49653a430cfa85f8f5730108d8ce1cd1ac8e23db7cd37cf4f9341

Aliases

arxiv: 2605.12855 · arxiv_version: 2605.12855v1 · doi: 10.48550/arxiv.2605.12855 · pith_short_12: 3PNIV66FWSLF · pith_short_16: 3PNIV66FWSLFHJBQ · pith_short_8: 3PNIV66F

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3PNIV66FWSLFHJBQZ6UF7D2XGA \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: dbda8afbc5b49653a430cfa85f8f5730108d8ce1cd1ac8e23db7cd37cf4f9341

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "678674179f846794e50beafb248452d63a4a463b0927135cf09db0faee472600",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T01:02:58Z",
    "title_canon_sha256": "7cdacb48929c11c844c8aaf8b896e540c1a9c7860ae449e156811838cca3accc"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12855",
    "kind": "arxiv",
    "version": 1
  }
}