Automatic segmentation and RECIST score evaluation in osteosarcoma using diffusion MRI: A computer aided system process

Kayal, Esha Baidya; Kandasamy, Devasenathipathy; Yadav, Richa; Bakhshi, Sameer; Sharma, Raju; Mehndiratta, Amit

doi:10.1016/j.ejrad.2020.109359

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Here, the patients were classified into four categories according to the changes in tumour diameter. The RECIST system includes complete response (CR, total disappearance of all target lesions), partial response (PR > 30% decrease in tumour diameter), progressive disease (PD > 20% increase or 5-mm absolute increase in tumour diameter) and stable disease (SD, a response somewhere between PR and PD) (Lubner et al 2017 ; Baidya et al 2020 ). The patients’ treatment response based on the 3D model tumour volume was evaluated as follows: CR, total disappearance of all target lesions; PR > 65% decrease in tumour volume; PD > 73% increase in tumour volume; SD, a response somewhere between PR and PD (Fleckenstein et al 2016 ; Choi et al 2017 ).…”

Section: Methodsmentioning

confidence: 99%

“…Renal cell cancer (RCC) is the most common primary tumour located in the kidney, accounting for up to 85% of all cases (Fleckenstein et al 2016 ). Although the 5-year survival rate of RCC has improved in recent years, the overall prognosis is still poor, especially for patients with late-stage disease (Baidya et al 2020 ). Currently, various combinations of highly selective tyrosine kinase inhibitors (TKIs), such as axitinib, and immune checkpoint inhibitors (ICIs), including avelumab, nivolumab and pembrolizumab, have been approved to treat adult patients with advanced RCC (Leslie et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Using three-dimensional model-based tumour volume change to predict the symptom improvement in patients with renal cell cancer

Fu,

Dong,

Zhang

et al. 2024

3 Biotech

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In our recent study, we explored the efficacy of three-dimensional (3D) measurement of tumor volume in predicting the improvement of quality of life (QoL) in patients suffering from renal cell cancer (RCC), who were treated with axitinib and anti-PD-L1 antibodies. This study encompassed 18 RCC patients, including 10 men and 8 women, with an average age of 56.83 ± 9.94 years. By utilizing 3D Slicer software, we analyzed pre- and post-treatment CT scans to assess changes in tumor volume. Patients' QoL was evaluated through the FKSI–DRS questionnaire. Our findings revealed that 3D models for all patients were successfully created, and there was a moderate agreement between treatment response classifications based on RECIST 1.1 criteria and volumetric analysis (kappa = 0.556, p = 0.001). Notably, nine patients reported a clinically meaningful improvement in QoL following the treatment. Interestingly, the change in tumor volume as indicated by the 3D model showed a higher area under the curve in predicting QoL improvement compared to the change in diameter measured by CT, although this difference was not statistically significant (z = 0.593, p = 0.553). Furthermore, a multivariable analysis identified the change in tumor volume based on the 3D model as an independent predictor of QoL improvement (odds ratio = 1.073, 95% CI 1.002–1.149, p = 0.045).In conclusion, our study suggests that the change in tumor volume measured by a 3D model may be a more effective predictor of symptom improvement in RCC patients than traditional CT-based diameter measurements. This offers a novel approach for assessing treatment response and patient well-being, presenting a significant advancement in the field of RCC treatment.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using three-dimensional model-based tumour volume change to predict the symptom improvement in patients with renal cell cancer

Fu,

Dong,

Zhang

et al. 2024

3 Biotech

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“…This assumes that lesions are grossly spherical and that their size represents their overall activity. To streamline the determination of this single diameter and make it less subject to possible humaninduced variability, semi-or fully-automated 2D or even 3D segmentation techniques can be applied to target lesions, which can also be semi-or fully-automatically tracked between scans acquired at different time points (129)(130)(131)(132)(133)(134). The 2D or 3D mask resulting from the segmentation process then readily permits the automated and accurate extraction of the largest diameter from the segmented lesion.…”

Section: Should (Could) Recist Be Automated?mentioning

confidence: 99%

Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group – ESOI Joint Paper

et al. 2022

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Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.

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“…Various software tools with different degrees of automation have been introduced to minimize misinterpretations [ 12 , 13 ]. Concepts of fully automated approaches including image interpretation and segmentation have been proposed [ 14 , 15 ] but have not been implemented in clinical trials so far. In our study, image interpretation and lesion definition were performed solely by a radiologist, while the assessment software provided automatic tumor burden calculation and the determination of the response according to iRECIST, hereby representing a semi-automated, software-assisted approach.…”

Section: Introductionmentioning

confidence: 99%

Tumor Response Evaluation Using iRECIST: Feasibility and Reliability of Manual Versus Software-Assisted Assessments

Ristow,

Well,

Wiese

et al. 2024

Cancers

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Objectives: To compare the feasibility and reliability of manual versus software-assisted assessments of computed tomography scans according to iRECIST in patients undergoing immune-based cancer treatment. Methods: Computed tomography scans of 30 tumor patients undergoing cancer treatment were evaluated by four independent radiologists at baseline (BL) and two follow-ups (FU), resulting in a total of 360 tumor assessments (120 each at BL/FU1/FU2). After image interpretation, tumor burden and response status were either calculated manually or semi-automatically as defined by software, respectively. The reading time, calculated sum of longest diameter (SLD), and tumor response (e.g., “iStable Disease”) were determined for each assessment. After complete data collection, a consensus reading among the four readers was performed to establish a reference standard for the correct response assignments. The reading times, error rates, and inter-reader agreement on SLDs were statistically compared between the manual versus software-assisted approaches. Results: The reading time was significantly longer for the manual versus software-assisted assessments at both follow-ups (median [interquartile range] FU1: 4.00 min [2.17 min] vs. 2.50 min [1.00 min]; FU2: 3.75 min [1.88 min] vs. 2.00 min [1.50 min]; both p < 0.001). Regarding reliability, 2.5% of all the response assessments were incorrect at FU1 (3.3% manual; 0% software-assisted), which increased to 5.8% at FU2 (10% manual; 1.7% software-assisted), demonstrating higher error rates for manual readings. Quantitative SLD inter-reader agreement was inferior for the manual compared to the software-assisted assessments at both FUs (FU1: ICC = 0.91 vs. 0.93; FU2: ICC = 0.75 vs. 0.86). Conclusions: Software-assisted assessments may facilitate the iRECIST response evaluation of cancer patients in clinical routine by decreasing the reading time and reducing response misclassifications.

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Automatic segmentation and RECIST score evaluation in osteosarcoma using diffusion MRI: A computer aided system process

Cited by 10 publications

References 14 publications

Using three-dimensional model-based tumour volume change to predict the symptom improvement in patients with renal cell cancer

Using three-dimensional model-based tumour volume change to predict the symptom improvement in patients with renal cell cancer

Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group – ESOI Joint Paper

Tumor Response Evaluation Using iRECIST: Feasibility and Reliability of Manual Versus Software-Assisted Assessments

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