Objective: To develop and validate magnetic resonance (MR) imaging-based radiomics models for high-risk endometrial cancer (EC) prediction preoperatively, to be able to estimate deep myometrial invasion (DMI) and lymphovascular space invasion (LVSI), and to discriminate between low-risk and other categories of risk as proposed by ESGO/ESTRO/ESP (European Society of Gynaecological Oncology—European Society for Radiotherapy & Oncology and European Society of Pathology) guidelines. Methods: This retrospective study included 96 women with EC who underwent 1.5-T MR imaging before surgical staging between April 2009 and May 2019 in two referral centers divided into training (T = 73) and validation cohorts (V = 23). Radiomics features were extracted using the MODDICOM library with manual delineation of whole-tumor volume on MR images (axial T2-weighted). Diagnostic performances of radiomic models were evaluated by area under the receiver operating characteristic (ROC) curve in training (AUCT) and validation (AUCV) cohorts by using a subset of the most relevant texture features tested individually in univariate analysis using Wilcoxon–Mann–Whitney. Results: A total of 228 radiomics features were extracted and ultimately limited to 38 for DMI, 29 for LVSI, and 15 for risk-classes prediction for logistic radiomic modeling. Whole-tumor radiomic models yielded an AUCT/AUCV of 0.85/0.68 in DMI estimation, 0.92/0.81 in LVSI prediction, and 0.84/0.76 for differentiating low-risk vs other risk classes (intermediate/high-intermediate/high). Conclusion: MRI-based radiomics has great potential in developing advanced prognostication in EC.

https://www.mdpi.com/2075-4426/12/11/1854/pdf?version=1667803513

Preoperative Tumor Texture Analysis on MRI for High-Risk Disease Prediction in Endometrial Cancer: A Hypothesis-Generating Study

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets. 

/pdf/what-benefit-can-be-obtained-from-magnetic-resonance-imaging-1yvob5te.pdf

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

BACKGROUND
Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC).


PURPOSE
To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC.


STUDY TYPE
Retrospective.


POPULATION
CC patients (n = 133) allocated into training(T) (nT  = 89)/validation(V) (nV  = 44) cohorts.


FIELD STRENGTH/SEQUENCE
T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T.


ASSESSMENT
Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps).


STATISTICAL TESTS
Radiomic signatures for prediction of DSS from T2WI (T2rad ) and T2WI with DWI (T2 + DWIrad ) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time-dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI-derived maximum tumor size ≤/> 4 cm (MAXsize ), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-II/III-IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan-Meier method with log-rank tests.


RESULTS
The radiomic signatures T2rad and T2 + DWIrad yielded AUCT /AUCV of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5-year DSS. Both signatures yielded better or equal prognostic performance to that of MAXsize (AUCT /AUCV : 0.69/0.65) and FIGO (AUCT /AUCV : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT /HRV for T2rad : 4.0/2.5 and T2 + DWIrad : 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts.


DATA CONCLUSION
Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.

MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.

We evaluated whether radiomic features extracted from planning computed tomography (CT) scans predict clinical end points in patients with locally advanced cervical cancer (LACC) undergoing intensity‐modulated radiation therapy and brachytherapy.

Treatment planning computed tomography radiomics for predicting treatment outcomes and haematological toxicities in locally advanced cervical cancer treated with radiotherapy: A retrospective cohort study

Abstract This study aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics extrapolated from the Ax LAVA + C series to identify intermediary- and high-risk factors in patients with cervical cancer undergoing surgery following neoadjuvant chemoradiotherapy. A total of 157 patients were divided into two groups: those without any intermediary- or high-risk factors and those with one intermediary-risk factor (negative group; n = 75). Those with any high-risk factor or more than one intermediary-risk factor (positive group; n = 82). Radiomics characteristics were extracted using Ax-LAVA + C MRI sequences. The data was divided into training (n = 126) and test (n = 31) sets in an 8:2 ratio. The training set data features were selected using the Mann–Whitney U test and the Least Absolute Shrinkage and Selection Operator (LASSO) test. The best radiomics features were then analyzed to build a preoperative predictive radiomics model for predicting intermediary- and high-risk factors in cervical cancer. Three models—the clinical model, the radiomics model, and the combined clinic and radiomics model—were developed in this study utilizing the random forest Algorithm. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity were used to assess the predictive efficacy and clinical benefits of each model. Three models were developed in this study to predict intermediary- and high-risk variables associated with postoperative pathology for patients who underwent surgery after receiving neoadjuvant radiation. In the training and test sets, the AUC values assessed using the clinical model, radiomics model, and combined clinical and radiomics models were 0.76 and 0.70, 0.88 and 0.86, and 0.91 and 0.89, respectively. The use of machine learning algorithms to analyze Delta Ax LAVA + C MRI radiomics features can aid in the prediction of intermediary- and high-risk factors in patients with cervical cancer receiving neoadjuvant therapy. 

Delta radiomics analysis for prediction of intermediary- and high-risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy

MRI-derived radiomics analysis improves the noninvasive pretreatment identification of multimodality therapy candidates with early-stage cervical cancer

Abstract Purpose: With the increasing detection rate of early prostate cancer (PCa), the proportion of surgical treatment is increasing. Surgery is the most effective treatment for PCa. Precise targeting of tumors during surgery can reduce the incidence of positive surgical margins (PSMs) and preserve the neurovascular bundles (NVBs) as much as possible. The objective of this study was to synthesize a PSMA fluorescent probe (PSMA-Cy5) and verify the targeting specificity of the probe for prostate cancer, thereby providing a theoretical basis for the development of PSMA fluorescent probes for clinical application in the future. Methods: In this study, a novel water-soluble 3H-indocyanine-type bioluminescent dye-Cy5-labeled prostate-specific membrane antigen (PSMA) ligand (PSMA-Cy5) was synthesized by liquid phase synthesis. The PSMA ligand was developed based on the glutamine-urea-lysine (Glu-urea-Lys) structure. The new fluorescent probe was evaluated in vitro and in vivo, and its safety was evaluated. Confocal microscopy was used to observe the binding uptake of PSMA-Cy5 with PSMA (+) LNCaP cells, PSMA (-) PC3 cells and blocked LNCaP cells. In in vivo optical imaging studies, the targeting specificity of PSMA (+) 22Rv1 tumors to probe binding was validated by tail vein injection of PSMA-Cy5. The safety of the PSMA-Cy5 probe was evaluated by histopathological analysis of mouse organs by a single high-dose tail vein injection of PSMA-Cy5. Results: In vitro fluorescence cell uptake experiments showed that the binding of PSMA-Cy5 to LNCaP cells has targeting specificity. PC3 cells and blocked LNCaP cells showed almost no uptake. The results of in vivo optical imaging studies showed that the tumor-to-background ratio in the 22Rv1 group was 3.39 ± 0.47; in the 22Rv1 blocking group it was 0.78 ± 0.15, and in the PC3 group it was 0.94 ± 0.09, consistent with the in vitro results. After a high-dose injection of PSMA-Cy5, there were no abnormalities in the tissues or organs of the mice. The probe showed good safety. Conclusions: PSMA-Cy5 is a probe with good targeting specificity and low toxicity that can accurately visualize tumors in vivo. This study has an important reference value for the development of PSMA fluorescent probes. In the future, it can be applied to precise tumor imaging during radical prostatectomy to reduce the incidence of postoperative PSM.

/pdf/a-preliminary-study-of-psma-fluorescent-probe-for-targeted-137x34iv.pdf

A Preliminary Study of PSMA Fluorescent Probe for Targeted Fluorescence Imaging of Prostate Cancer

The depth of cervical stromal invasion is one of the important prognostic factors affecting decision-making for early stage cervical cancer (CC). This study aimed to develop and validate a T2-weighted imaging (T2WI)-based radiomics model and explore independent risk factors (factors with statistical significance in both univariate and multivariate analyses) of middle or deep stromal invasion in early stage CC.Between March 2017 and March 2021, a total of 234 International Federation of Gynecology and Obstetrics IB1-IIA1 CC patients were enrolled and randomly divided into a training cohort (n = 188) and a validation cohort (n = 46). The radiomics features of each patient were extracted from preoperative sagittal T2WI, and key features were selected. After independent risk factors were identified, a combined model and nomogram incorporating radiomics signature and independent risk factors were developed. Diagnostic accuracy of radiologists was also evaluated.The maximal tumor diameter (MTD) on magnetic resonance imaging was identified as an independent risk factor. In the validation cohort, the radiomics model, MTD, and combined model showed areas under the curve of 0.879, 0.844, and 0.886. The radiomics model and combined model showed the same sensitivity and specificity of 87.9% and 84.6%, which were better than radiologists (sensitivity, senior = 75.7%, junior = 63.6%; specificity, senior = 69.2%, junior = 53.8%) and MTD (sensitivity = 69.7%, specificity = 76.9%).MRI-based radiomics analysis outperformed radiologists for the preoperative diagnosis of middle or deep stromal invasion in early stage CC, and the probability can be individually evaluated by a nomogram. 

/pdf/mri-based-radiomics-analysis-improves-preoperative-39mu4iao.pdf

MRI-based radiomics analysis improves preoperative diagnostic performance for the depth of stromal invasion in patients with early stage cervical cancer

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MRI-derived radiomics analysis improves the noninvasive pretreatment identification of multimodality therapy candidates with early-stage cervical cancer

A Preliminary Study of PSMA Fluorescent Probe for Targeted Fluorescence Imaging of Prostate Cancer

MRI-based radiomics analysis improves preoperative diagnostic performance for the depth of stromal invasion in patients with early stage cervical cancer