Evidence map›Paper›PMID 36017768›Full record

ArticleOrthopaedic surgery2022

A Scoring System for Predicting Neoadjuvant Chemotherapy Response in Primary High-Grade Bone Sarcomas: A Multicenter Study.

Fangzhou He, Lu Xie, Xin Sun, Jie Xu, Yuan Li, Rong Liu, Kunkun Sun, Danhua Shen, Jin Gu, Tao Ji and 1 more

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in Orthopaedic surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Fangzhou HeMusculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China.ORCID https://orcid.org/0000-0003-1297-6484
Lu XieMusculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China.
Xin SunMusculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China.
Jie XuMusculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China.
Yuan LiDepartment of Radiology, Peking University People's Hospital, Beijing, China.
Rong LiuDepartment of Radiology, Peking University People's Hospital, Beijing, China.
Kunkun SunDepartment of Pathology, Peking University People's Hospital, Beijing, China.
Danhua ShenDepartment of Pathology, Peking University People's Hospital, Beijing, China.
Jin GuDepartment of Surgical Oncology, Peking University Shougang Hospital, Beijing, China.
Tao JiMusculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China.
Wei GuoMusculoskeletal Tumor Center, Peking University People's Hospital, Beijing, China.ORCID https://orcid.org/0000-0003-2471-5767
Peking University · CNPeking University Shougang Hospital · CN

Funding

Medical Service and Security Capacity Enhancement Project 2199000730National Natural Science Foundation of China 82072970
6 · The paper itself

Abstract

objectiveCurrently, there is a lack of good clinical tools for evaluating the effect of chemotherapy preoperatively on primary high-grade bone sarcomas. Our goal was to investigate the predictive value of the clinical findings and establish a scoring system to predict chemotherapy response.

methodsWe conducted a retrospective multicenter cohort study and reviewed 322 patients with primary high-grade bone sarcomas. Patients who routinely received neoadjuvant chemotherapy and underwent primary tumor resection with an assessment of tumor necrosis rate (TNR) were enrolled in this study. The medical records of patients were collected from November 1, 2011, to March 1, 2018, at Peking University People's Hospital (PKUPH) and Peking University Shougang Hospital (PKUSH). The mean age of the patients was 16.2 years (range 3-52 years), of whom 65.5% were male. The clinical data collected before and after neoadjuvant chemotherapy included the degree of pain, laboratory inspection, X-ray, CT, contrast-enhanced magnetic resonance (MR), and positron emission tomography-computed tomography (PET-CT). Several machine learning models, including logistic regression, decision trees, support vector machines, and neural networks, were used to classify the chemotherapy responses. Area under the curve (AUC) of the scoring system to predict chemotherapy response is the primary outcome measure.

resultsFor patients without events, a minimum follow-up of 24 months was achieved. The median follow-up time was 43.3 months, and it ranged from 24 to 84 months. The 5 years progression-free survival (PFS) of the included patients was 54.1%. The 5 years PFS rate was 39.7% for poor responders and 74.9% for good responders. Features such as longest diameter reduction ratio (up to three points), clear bone boundary formation (up to two points), tumor necrosis measured by magnetic resonance (up to two points), maximum standard uptake value (SUV

conclusionWe first devised a well-performing comprehensive scoring system to predict the response to neoadjuvant chemotherapy in primary high-grade bone sarcomas.

Indexed as

Neoadjuvant TherapySarcomaAdolescentAdultAlkaline PhosphataseChildChild, PreschoolCohort StudiesFluorodeoxyglucose F18HumansMiddle AgedNecrosisPositron Emission Tomography Computed TomographyRadiopharmaceuticalsRetrospective StudiesTreatment OutcomeAlkaline PhosphataseFluorodeoxyglucose F18RadiopharmaceuticalsBone sarcomasChemotherapy responseDiagnosis supportMachine learningScoring system

Identifiers

PMID36017768
PMCPMC9531107
OpenAlexW4293194524

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.