Functional MRI radiomics-based assessment of pelvic bone marrow changes after concurrent chemoradiotherapy for cervical cancer

Qin, Xiaohang; Gong, Guanzhong; Wang, Lizhen; Su, Ya; Yin, Yong

doi:10.1186/s12885-022-10254-7

Cited by 3 publications

(3 citation statements)

References 32 publications

(39 reference statements)

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“…In addition, when two patient groups were combined together, the HT prediction model performed slightly lower than the model in the CRT patients but better than the model in the RT-alone patients. These results consistently indicate that the HT occurrence in the RT-alone group may be regulated by some other latent factors in addition to the radiomics and dosimetric factors [e.g., radiomics of 3D dose distribution ( 35 , 36 ) or MRI-based radiomics ( 37 , 38 )], which needs to be investigated in the future.…”

Section: Discussionsupporting

confidence: 53%

Acute hematologic toxicity prediction using dosimetric and radiomics features in patients with cervical cancer: does the treatment regimen matter?

Yue,

Li,

You

et al. 2024

Front. Oncol.

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BackgroundAcute hematologic toxicity (HT) is a prevalent adverse tissue reaction observed in cervical cancer patients undergoing chemoradiotherapy (CRT), which may lead to various negative effects such as compromised therapeutic efficacy and prolonged treatment duration. Accurate prediction of HT occurrence prior to CRT remains challenging.MethodsA discovery dataset comprising 478 continuous cervical cancer patients (140 HT patients) and a validation dataset consisting of 205 patients (52 HT patients) were retrospectively enrolled. Both datasets were categorized into the CRT group and radiotherapy (RT)-alone group based on the treatment regimen, i.e., whether chemotherapy was administered within the focused RT duration. Radiomics features were derived by contouring three regions of interest (ROIs)—bone marrow (BM), femoral head (FH), and clinical target volume (CTV)—on the treatment planning CT images before RT. A comprehensive model combining the radiomics features as well as the demographic, clinical, and dosimetric features was constructed to classify patients exhibiting acute HT symptoms in the CRT group, RT group, and combination group. Furthermore, the time-to-event analysis of the discriminative ROI was performed on all patients with acute HT to understand the HT temporal progression.ResultsAmong three ROIs, BM exhibited the best performance in classifying acute HT, which was verified across all patient groups in both discovery and validation datasets. Among different patient groups in the discovery dataset, acute HT was more precisely predicted in the CRT group [area under the curve (AUC) = 0.779, 95% CI: 0.657–0.874] than that in the RT-alone (AUC = 0.686, 95% CI: 0.529–0.817) or combination group (AUC = 0.748, 95% CI: 0.655–0.827). The predictive results in the validation dataset similarly coincided with those in the discovery dataset: CRT group (AUC = 0.802, 95% CI: 0.669–0.914), RT-alone group (AUC = 0.737, 95% CI: 0.612–0.862), and combination group (AUC = 0.793, 95% CI: 0.713–0.874). In addition, distinct feature sets were adopted for different patient groups. Moreover, the predicted HT risk of BM was also indicative of the HT temporal progression.ConclusionsHT prediction in cervical patients is dependent on both the treatment regimen and ROI selection, and BM is closely related to the occurrence and progression of HT, especially for CRT patients.

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Section: Discussionsupporting

confidence: 53%

Acute hematologic toxicity prediction using dosimetric and radiomics features in patients with cervical cancer: does the treatment regimen matter?

Yue,

Li,

You

et al. 2024

Front. Oncol.

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“…Another interesting and widely studied field of radiomics research in patients with CC is the assessment of response to treatments including conization [98], radiotherapy, chemotherapy, and chemo-radiation therapy [99][100][101][102][103][104][105][106][107][108][109], the evaluation of recurrence [110][111][112][113], and toxicity related to radiotherapy [114][115][116]. Among all studies on the assessment of response to treatments, there are four multicentric studies and one prospective study that deserve attention (Table 2).…”

Section: Response To Treatmentmentioning

confidence: 99%

“…Finally, the multicentre retrospective study by Gui et al [104] analysed 183 patients from two institutions and created a radiomics model to predict pathological complete response after neoadjuvant chemoradiotherapy, yielding promising results (the RF_DEF model showed a mean AUC of 0.80). It is also interesting to mention that some studies have investigated the role of radiomics in predicting malnutrition after intervention [117], as well as haematological toxicities, changes in the pelvic bone marrow, and proctitis related to radiotherapy [114][115][116].…”

Section: Response To Treatmentmentioning

confidence: 99%

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

Franco,

Vernuccio,

Maino

et al. 2023

Applied Sciences

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Radiomics is an emerging field of research based on extracting mathematical descriptive features from medical images with the aim of improving diagnostic performance and providing increasing support to clinical decisions. In recent years, a number of studies have been published regarding different possible applications of radiomics in gynaecological imaging. Many fields have been explored, such as tumour diagnosis and staging, differentiation of histological subtypes, assessment of distant metastases, prediction of response to therapy, recurrence, and patients’ outcome. However, several studies are not robust, do not include validation cohorts, or lack reproducibility. On these bases, the purpose of this narrative review is to provide an overview of the most relevant studies in the literature on radiomics in gynaecological imaging. We focused on gynaecological malignancies, particularly endometrial, cervical, mesenchymal, and ovarian malignant pathologies.

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Association between IDEAL-IQ MRI fat fraction quantification and pelvic bone marrow reserve function in concurrent chemoradiotherapy for cervical cancer

Wen

Qin

et al. 2023

Radiation Medicine and Protection

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Functional MRI radiomics-based assessment of pelvic bone marrow changes after concurrent chemoradiotherapy for cervical cancer

Cited by 3 publications

References 32 publications

Acute hematologic toxicity prediction using dosimetric and radiomics features in patients with cervical cancer: does the treatment regimen matter?

Acute hematologic toxicity prediction using dosimetric and radiomics features in patients with cervical cancer: does the treatment regimen matter?

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

Association between IDEAL-IQ MRI fat fraction quantification and pelvic bone marrow reserve function in concurrent chemoradiotherapy for cervical cancer

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