CRISPS: A Pictorial Essay of an Acronym to Interpreting Metastatic Head and Neck Lymphadenopathy
Bibliographic record
Abstract
Lymph node metastasis in head and neck cancer isa significant prognostic indicator that has a major impactboth on treatment planning and patient survival [1]. Becauseaggressive treatment for primary malignancy has becomemore advanced, patients often live long enough to experienceeither recurrence or distant metastasis. Nodal disease and,particularly, its presence on first presentation is the mostreliable predictor for both of these phenomena [2e5]. Withregard to the natural history of this head and neck cancer, themost common sites of metastasis from positive cervicallymph nodes are the lungs, bones, and liver [6].The rate of metastasis varies among different areas of theaerodigestive tract. For instance, T3/T4 carcinomas of theoral cavity, oropharynx, hypopharynx, and supraglotticlarynx exhibit ipsilateral nodal metastasis at a rate of higherthan 50% [7]. The rate of either bilateral or contralateralnodal metastasis ranges from 2%-35% [8,9]. Radiologicidentification of nodal disease thus is critical to guidesurgical decision making regarding neck dissection becauseimaging has been shown to identify metastasis in 7.5%-19%of clinically silent nodes [2,3,9].The identification of nodal disease is important withregard to both the pre- and posttreatment stages. Pretreat-ment imaging has been shown to identify areas of involve-ment in the retropharyngeal, high level II, low level IV, lowlevel V, and level VI/VII nodes [7]. As a result, pretreatmentcomputed tomography (CT) or magnetic resonance imaging(MRI) has become a mainstay of the care plan for patientswith head and neck cancer. The wide acceptance of theradiologic definition of nodal levels has expedited thistransition. Diseased lymph nodes are identified radiologi-cally by their clustering, roundness (shape), inhomogeneity,size, and periphery (extracapsular spread). In addition,the radiologist should be familiar with the most probablesentinel nodes for a given malignancy. For this reason, wepropose the acronym ‘‘CRISPS’’ (clustering, rounded shape,inhomogeneity, size, periphery, sentinel location) asa comprehensive and easy-to-remember mnemonic to aid theradiologist in identifying nodal involvement in patients withhead and neck cancer.Contrast-enhanced CT represents the ideal modalityfor the assessment of metastatic lymphadenopathy, followedclosely by magnetic resonance (MR) pulse sequences(unenhanced T1 or T2 with fat saturation) [10]. Positronemission tomographyeCT possesses the advantage ofmetabolic correlation, which can aid both in the setting ofequivocal findings and distant disease. However, challenges
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.025 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".