Evidence map›Paper›PMID 32086578›Full record

ArticleEuropean radiology2020

SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging.

Esha Baidya Kayal, Devasenathipathy Kandasamy, Raju Sharma, Mehar C Sharma, Sameer Bakhshi, Amit Mehndiratta

Abstract read
PubMed Publisher
In one paragraph

Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Mci-nnunet: multi-cue integration in nnunet for shape-aware segmentation of the femoral bone tumors.International journal of computer assisted radiology and surgery · 2026
    Article
  2. Review
  3. Article
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

6 authors at 3 institutions in 1 country.

Esha Baidya KayalCentre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
Devasenathipathy KandasamyDepartment of Radiology, All India Institute of Medical Sciences, New Delhi, India.
Raju SharmaDepartment of Radiology, All India Institute of Medical Sciences, New Delhi, India.
Mehar C SharmaDepartment of Pathology, All India Institute of Medical Sciences, New Delhi, India.
Sameer BakhshiDepartment of Medical Oncology, Dr. B.R. Ambedkar Institute-Rotary Cancer Hospital (IRCH), All India Institute of Medical Sciences, New Delhi, India.
Amit MehndirattaCentre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India. amit.mehndiratta@keble.oxon.org.ORCID http://orcid.org/0000-0001-6477-2462
All India Institute of Medical Sciences · INDR. B.R.A. Institute Rotary Cancer Hospital · INIndian Institute of Technology Delhi · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveHistopathological examination (HPE) is the current gold standard for assessing chemotherapy response to tumor, but it is possible only after surgery. The purpose of the study was to develop a noninvasive, imaging-based robust method to delineate, visualize, and quantify the proportions of necrosis and viable tissue present within the tumor along with peritumoral edema before and after neoadjuvant chemotherapy (NACT) and to evaluate treatment response with correlation to HPE necrosis after surgery.

methodsThe MRI dataset of 30 patients (N = 30; male:female = 24:6; age = 17.6 ± 2.7 years) with osteosarcoma was acquired using 1.5 T Philips Achieva MRI scanner before (baseline) and after 3 cycles of NACT (follow-up). After NACT, all patients underwent surgical resection followed by HPE. Simple linear iterative clustering supervoxels and Otsu multithresholding were combined to develop the proposed method-SLICs+MTh-to subsegment and quantify viable and nonviable regions within tumor using multiparametric MRI. Manually drawn ground-truth ROIs and SLICs+MTh-based segmentation of tumor, edema, and necrosis were compared using Jacquard index (JI), Dice coefficient (DC), precision (P), and recall (R). Postcontrast T1W images (PC-T1W) were used to validate the SLICs+MTh-based necrosis. SLICs+MTh-based necrosis volume at follow-up was compared with HPE necrosis using paired t test (p ≤ 0.05).

resultsActive tumor, necrosis, and edema were segmented with moderate to satisfactory accuracy (JI = 62-78%; DC = 72-87%; P = 67-87%; R = 63-88%). Qualitatively and quantitatively (DC = 74 ± 9%), the SLICs+MTh-based necrosis area correlated well with the hypointense necrosis areas in PC-T1W. No significant difference (paired t test, p = 0.26; Bland-Altman plot, bias = 2.47) between SLICs+MTh-based necrosis at follow-up and HPE necrosis was observed.

conclusionThe proposed multiparametric MRI-based SLICs+MTh method performs noninvasive assessment of NACT response in osteosarcoma that may improve cancer treatment monitoring, planning, and overall prognosis. KEY POINTS: • The simple linear iterative clustering supervoxels and Otsu multithresholding-based technique (SLICs+MTh) successfully estimates the proportion of necrosis, viable tumor, and edema in osteosarcoma in the course of chemotherapy. • The proposed technique is noninvasive and uses multiparametric MRI to measure necrosis as an indication of anticancer treatment response. • SLICs+MTh-based necrosis was in satisfactory agreement with histological necrosis after surgery.

Indexed as

AdolescentAntineoplastic AgentsBone NeoplasmsDiffusion Magnetic Resonance ImagingFemaleHumansMaleNeoadjuvant TherapyOsteosarcomaPrognosisAntineoplastic AgentsChemotherapyComputer-assisted decision-makingMagnetic resonance imagingOsteosarcomaTreatment outcome

Identifiers

PMID32086578
OpenAlexW3008971234

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.