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CORR Insights®: Is Navigation-guided En Bloc Resection Advantageous Compared With Intralesional Curettage for Locally Aggressive Bone Tumors?

2018· letter· en· W2800048814 on OpenAlexaboutno aff
Darin Davidson

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2018
Typeletter
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCurettageSurgeryFixation (population genetics)CryotherapyProsthesisFibulaTibia

Abstract

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Where Are We Now? Benign but aggressive bone lesions, most commonly giant cell tumor of bone, often occur in the epiphyses and metaphyses of long bones, potentially decreasing adjacent-joint function and quality of life. In addition, these tumors can compromise bone stock (especially if they recur), making subsequent joint salvage with standard arthroplasty techniques challenging. Because the patients who get these tumors often are young, complications associated with treatment can linger for years. Most of these tumors can be treated with of curettage, sometimes combined with adjuvant treatments, such as phenol, cryotherapy, argon beam laser, and reconstruction with bone graft or cement and possibly plate fixation. Alternatively, en bloc resection of the tumor can be considered, particularly in sites which are not structurally critical such as the fibula. Though the indications have not yet been widely adopted, some advocate resection of tumors in structural sites followed by reconstruction with either allograft or a prosthesis. The advantage of curettage is that it preserves the adjacent joint, but it may increase the risk of local recurrence. By contrast, en bloc resection reduces that risk, but the reconstructions are more involved, and may be associated with more complications, both early and later on. Therefore, choosing a treatment calls for a balance between local control and functional considerations. Farfalli and colleagues compared curettage and bone graft reconstruction with navigated en bloc resection followed by fresh-frozen allograft reconstruction in patients with benign, aggressive bone lesions; they found no difference between the treatments in local recurrence, complications, or function as measured by the Musculoskeletal Tumor Society (MSTS) score [2]. Other studies on curettage and reconstruction of these lesions have reported a low risk of local recurrence [8,9]. There have, however, been few studies directly comparing curettage with en bloc resection of benign aggressive bone lesions. Guo and colleagues [4] compared intralesional curettage with resection of acetabular giant cell tumor and reported similar functional outcome on the MSTS score [2], higher complications with resection, and decreased local recurrence with resection. The use of navigation has been shown to be associated with more accurate osteotomies, thereby preserving both the margin around the tumor and bone stock [5-7]. Where Do We Need To Go? Although some studies [5,6] have focused on malignant lesions where obtaining a negative margin is essential, the paradigm of treatment is different with benign aggressive lesions because local recurrence is not correlated with worse overall survival as it is with malignant lesions [3,10]. Studies [8,9] have shown that treatment with curettage and reconstruction is effective in providing local control of the tumor amongst the more-common benign, aggressive lesions. That being so, there should be consideration of the balance in exposing these generally young patients whose tumors are unlikely to be life threatening to the lifelong downsides associated with major reconstructions that follow en-bloc resections. But if we are to answer this question more definitively, a patient-reported outcomes tool should be utilized rather than a clinician-completed one such as the MSTS score in order to incorporate the patient perspective into the analysis. The Toronto Extremity Salvage Score (TESS) [1] might work well for this, and we might supplement it with an outcomes tool that focuses on overall health-related quality of life (HRQL). Should en bloc resection be found to be the preferred treatment method, then it becomes necessary to determine whether such resection should be performed with or without navigation. In part, this determination may be site specific as the technical complexities of a pelvic resection is different compared to appendicular sites. How Do We Get There? Answering these important questions is challenging because of the relative rarity of these tumors, making adequate sample sizes difficult to come by; researchers will need to perform collaborative, multicenter studies to overcome this. A comparative study between curettage and resection, stratified on the anatomic location, will provide the necessary data to determine the better treatment strategy. These studies should incorporate oncologic outcomes, specifically local control, complications, and a measure of patient-reported outcome, as well as function and HRQL. Researchers should consider cost-effectiveness studies comparing en-bloc resection to intralesional treatments. Such studies should examine: (1) The costs of reconstruction with large-segment allografts, (2) prosthetic reconstructions, (3) future revisions of these constructs, which are complications unique to en-bloc resection, and (4) treating recurrences, which are likely to be more common following intralesional treatment. Finally, if en-bloc resection is shown to be superior (either in terms of efficacy or cost-effectiveness), then we should investigate whether such resections benefit from computer navigation. These studies should focus on one anatomic site (such as the pelvis), or, if the sample sizes are large enough, the analyses could be stratified by anatomic site. Because navigation is expensive, and it adds surgical time, cost effectiveness of navigation will need to be assessed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0210.004

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.

Opus teacher head0.079
GPT teacher head0.423
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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