Net Reclassification Improvement and Integrated Discrimination Improvement

Miller, Todd D.; Askew, J. Wells

doi:10.1161/circimaging.113.000797

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…The IDI assessment does not require the categories for models. 24 To assess reclassification improvement, we defined 3 risk categories among the population, based on the tertile distribution of MAGGICs (low risk: <17.5%; intermediate risk: 17.5-29.2%; high risk: ≥29.2%) and SHFM (low risk: >92.4%; intermediate risk: 85.9-92.4%; high risk: ≤85.9%). 5 Power analysis was performed to estimate the required number of patients based on previous study.…”

Section: Study Populationmentioning

confidence: 99%

Prognostic Significance of Peripheral Microvascular Endothelial Dysfunction in Heart Failure With Reduced Left Ventricular Ejection Fraction

Fujisue

Sugiyama

Matsuzawa

et al. 2015

Circ J

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Section: Study Populationmentioning

confidence: 99%

Prognostic Significance of Peripheral Microvascular Endothelial Dysfunction in Heart Failure With Reduced Left Ventricular Ejection Fraction

Fujisue

Sugiyama

Matsuzawa

et al. 2015

Circ J

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“…NRI offered a meaningful index of classification accuracy, quantifying the amount of correct reclassified samples introduced by radiomic signature. IDI indicated integrated discrimination improvement [25]. The p-values associated with NRI and IDI indicated whether the improvement of reclassification after the inclusion of radiomic signature was statistically significant.…”

Section: Methodsmentioning

confidence: 97%

Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study

Yao

Liu

et al. 2019

EBioMedicine

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BackgroundIdentification of pregnancies with postpartum haemorrhage (PPH) antenatally rather than intrapartum would aid delivery planning, facilitate transfusion requirements and decrease maternal complications. MRI has been increasingly used for placenta evaluation. Here, we aim to build a nomogram incorporating both clinical and radiomic features of placenta to predict the risk for PPH in pregnancies during caesarian delivery (CD).MethodsA total of 298 pregnant women were retrospectively enrolled from Henan Provincial People's Hospital (training cohort: n = 207) and from The Third Affiliated Hospital of Zhengzhou University (external validation cohort: n = 91). These women were suspected with placenta accreta spectrum (PAS) disorders and underwent MRI for placenta evaluation. All of them underwent CD and were singleton. PPH was defined as more than 1000 mL estimated blood loss (EBL) during CD. Radiomic features were selected based on their correlations with EBL. Radiomic, clinical, radiological, clinicoradiological and clinicoradiomic models were built to predict the risk of PPH for each patient. The model with the best prediction performance was validated with its discrimination ability, calibration curve and clinical application.FindingsThirty-five radiomic features showed strong correlation with EBL. The clinicoradiomic model resulted in the best discrimination ability for risk prediction of PPH, with AUC of 0.888 (95% CI, 0.844–0.933) and 0.832 (95% CI, 0.746–0.913), sensitivity of 91.2% (95% CI, 85.8%-96.7%) and 97.6% (95% CI, 92.7%-100%) in the training and validation cohort respectively. For patients with severe PPH (EBL more than 2000 mL), 53 out of 55 pregnancies (96.4%) in the training cohort and 18 out of 18 (100%) pregnancies in the validation cohort were identified by the clinicoradiomic model. The model performed better in patients without placenta previa (PP) than in patients with PP, with AUC of 0.983 compared with 0.867, sensitivity of 100% compared with 90.8% in the training cohort, AUC of 0.832 compared with 0.815, sensitivity of 97.6% compared with 97.2% in the validation cohort.InterpretationThe clinicoradiomic model incorporating both prenatal clinical factors and radiomic signature of placenta on T2WI showed good performance for risk prediction of PPH. The predictive model can identify severe PPH with high sensitivity and can be applied in patients with and without PP.

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“…F ‐test was used to test whether there is a significant difference in the variance used to the two sets of data. In order to evaluate the introduction of different MRI input sequence information to the improvement of model performance, net reclassification improvement (NRI) is used to quantitative comparison of assessing adverse pregnancy outcome between different models 23 . The correlation between invasive PA and adverse outcomes was calculated by Pearson correlation coefficient.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Model Based on Multisequence MRI Images for Assessing Adverse Pregnancy Outcome in Placenta Accreta

Zong,

Pei,

Yan

et al. 2023

Magnetic Resonance Imaging

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BackgroundPreoperative assessment of adverse outcomes risk in placenta accreta spectrum (PAS) disorders is of high clinical relevance for perioperative management and prognosis.PurposeTo investigate the association of preoperative MRI multisequence images and adverse pregnancy outcomes by establishing a deep learning model in patients with PAS.Study TypeRetrospective.Population323 pregnant women (age from 20 to 46, the median age is 33), suspected of PAS, underwent MRI to assess the PAS, divided into the training (N = 227) and validation datasets (N = 96).Field Strength/Sequence1.5T scanner/fast imaging employing steady‐state acquisition sequence and single shot fast spin echo sequence.AssessmentDifferent deep learning models (i.e., with single MRI input sequence/two sequences/multisequence) were compared to assess the risk of adverse pregnancy outcomes, which defined as intraoperative bleeding ≥1500 mL and/or hysterectomy. Net reclassification improvement (NRI) was used for quantitative comparison of assessing adverse pregnancy outcome between different models.Statistical TestsThe AUC, sensitivity, specificity, and accuracy were used for evaluation. The Shapiro–Wilk test and t‐test were used. A P value of <0.05 was considered statistically significant.Results215 cases were invasive placenta accreta (67.44% of them with adverse outcomes) and 108 cases were non‐invasive placenta accreta (9.25% of them with adverse outcomes). The model with four sequences assessed adverse pregnancy outcomes with AUC of 0.8792 (95% CI, 0.8645–0.8939), with ACC of 85.93% (95%, 84.43%–87.43%), with SEN of 86.24% (95% CI, 82.46%–90.02%), and with SPC of 85.62% (95%, 82.00%–89.23%) on the test cohort. The performance of model with four sequences improved above 0.10 comparing with that of model with two sequences and above 0.20 comparing with that of model with single sequence in terms of NRI.Data ConclusionThe proposed model showed good diagnostic performance for assessing adverse pregnancy outcomes.Level of Evidence3Technical EfficacyStage 2

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Net Reclassification Improvement and Integrated Discrimination Improvement

Cited by 12 publications

References 16 publications

Prognostic Significance of Peripheral Microvascular Endothelial Dysfunction in Heart Failure With Reduced Left Ventricular Ejection Fraction

Prognostic Significance of Peripheral Microvascular Endothelial Dysfunction in Heart Failure With Reduced Left Ventricular Ejection Fraction

Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: A multicentre study

Deep Learning Model Based on Multisequence MRI Images for Assessing Adverse Pregnancy Outcome in Placenta Accreta

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