Second Primary Breast Cancer in Young Breast Cancer Survivors

Brantley, Kristen D.; Rosenberg, Shoshana M.; Collins, Laura C.; Ruddy, Kathryn J.; Tamimi, Rulla M.; Schapira, Lidia; Borges, Virginia F.; Warner, Ellen; Come, Steven E.; Zheng, Yue; Kirkner, Gregory J.; Snow, Craig; Winer, Eric P.; Partridge, Ann H.

doi:10.1001/jamaoncol.2024.0286

Cited by 1 publication

(1 citation statement)

References 42 publications

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“…ROI displaying the best model classification was further used to study the temporal progression of HT using time-to-event analysis ( 33 , 34 ). The model prediction probability of discriminative ROIs was used as HT risk indicators.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%