Spliced FKBP51s predicts unfavorable prognosis of glioblastoma patients.

The primary treatment for glioblastoma (GBM) is removing the tumor mass as defined by magnetic resonance imaging (MRI). However, MRI has limited diagnostic and predictive value. Tumor-associated macrophages (TAMs) are abundant in GBM microenvironment (TME) and are found in peripheral blood (PB). FKBP51 expression, with its canonical and spliced isoforms, is constitutive in immune cells and aberrant in GBM. Spliced FKBP51s supports M2-polarization. To find an immunological signature that combined with MRI could advance in diagnosis, we immunophenotyped the macrophages of TME and PB from 37 GBM patients using FKBP51s and classical M1-M2 markers. We also determined the tumor levels of FKBP51s, PD-L1, and HLA-DR. Tumors expressing FKBP51s showed an increase in various M2 phenotypes and Tregs in PB, indicating immunosuppression. Tumors expressing FKBP51s also activated STAT3 and were associated with reduced survival. Correlative studies with MRI and tumor/macrophages co-cultures allowed to interpret TAMs. Tumor volume correlated with M1 infiltration of TME. Co-cultures with spheroids produced M1 polarization, suggesting that M1 macrophages may infiltrate alongside cancer stem-cells. Co-cultures of adherent cells developed the M2 phenotype CD163/FKBP51s expressing pSTAT6, a transcription factor enabling migration and invasion. In patients with recurrences, increased counts of CD163/FKBP51s monocyte/macrophages in PB correlated with callosal infiltration and was accompanied by a concomitant decrease in TME-infiltrating M1 macrophages. PB PD-L1/FKBP51s connoted necrotic tumors. In conclusion, FKBP51s identifies a GBM subtype that significantly impairs the immune system. Moreover, FKBP51s marks PB macrophages associated with MRI features of glioma malignancy that can aid in patient monitoring.

Is PET Radiomics Useful to Predict Pathologic Tumor Response and Prognosis in Locally Advanced Cervical Cancer?

This study investigated whether radiomic features extracted from pretreatment [18F]FDG PET could improve the prediction of both histopathologic tumor response and survival in patients with locally advanced cervical cancer (LACC) treated with neoadjuvant chemoradiotherapy followed by surgery compared with conventional PET parameters and histopathologic features. Methods: The medical records of all consecutive patients with LACC referred between July 2010 and July 2016 were reviewed. [18F]FDG PET/CT was performed before neoadjuvant chemoradiotherapy. Radiomic features were extracted from the primary tumor volumes delineated semiautomatically on the PET images and reduced by factor analysis. A receiver-operating-characteristic analysis was performed, and conventional and radiomic features were dichotomized with Liu's method according to pathologic response (pR) and cancer-specific death. According to the study protocol, only areas under the curve of more than 0.70 were selected for further analysis, including logistic regression analysis for response prediction and Cox regression analysis for survival prediction. Results: A total of 195 patients fulfilled the inclusion criteria. At pathologic evaluation after surgery, 131 patients (67.2%) had no or microscopic (≤3 mm) residual tumor (pR0 or pR1, respectively); 64 patients (32.8%) had macroscopic residual tumor (>3 mm, pR2). With a median follow-up of 76.0 mo (95% CI, 70.7-78.7 mo), 31.3% of patients had recurrence or progression and 20.0% died of the disease. Among conventional PET parameters, SUVmean significantly differed between pathologic responders and nonresponders. Among radiomic features, 1 shape and 3 textural features significantly differed between pathologic responders and nonresponders. Three radiomic features significantly differed between presence and absence of recurrence or progression and between presence and absence of cancer-specific death. Areas under the curve were less than 0.70 for all parameters; thus, univariate and multivariate regression analyses were not performed. Conclusion: In a large series of patients with LACC treated with neoadjuvant chemoradiotherapy followed by surgery, PET radiomic features could not predict histopathologic tumor response and survival. It is crucial to further explore the biologic mechanism underlying imaging-derived parameters and plan a large, prospective, multicenter study with standardized protocols for all phases of the process of radiomic analysis to validate radiomics before its use in clinical routine.

Hug sign in intraprocedural cone-beam-CT to predict short-term response to combined treatment of hepatocellular carcinoma.

OBJECTIVES: Combined treatment of ablation and chemoembolization for hepatocellular carcinoma represents a promising therapy to increase treatment efficacy and improve patient survival. The "hug sign" is a recently introduced radiological sign consisting in deposition of beads/contrast agent during transarterial chemoembolization in the hyperemic area surrounding the post-ablation volume, seen during intraprocedural unenhanced cone-beam CT, that may indicate intraprocedural success. Aim of our retrospective study was to analyze the usefulness of the "hug sign" at the intraprocedural unenhanced cone-beam CT as an early predictor of response to combined treatment, based on the hug sign angle. MATERIALS AND METHODS: Between January 2017 and September 2021 all patients with hepatocellular carcinoma which underwent a combined treatment of thermal ablation followed by chemoembolization were enrolled. All treated patients underwent immediate post-procedural unenhanced cone-beam CT to evaluate the deposition of contrast agent, lipiodol or radiopaque beads and to assess the percentage of coverage of the ablated area with the contrast agent (hug sign angle). Patients with missing pre-procedural, intra-procedural and/or post-procedural data/imaging, or with poor-quality post-procedural cone-beam CT images were excluded. RESULTS: 128 patients (mean age, 69.3 years ± 1.1 [standard deviation]; 87 men) were evaluated. Our study evidenced that 84.4% (81/85) of patients with a hug sign angle of 360° had no residual tumor at the first 1-/3-months follow-up examination. A hug sign angle of 360° also showed to be an independent protective factor against residual tumor at multivariate analysis. CONCLUSION: Unenhanced cone-beam CT performed at the end of a combined treatment with ablation plus chemoembolization can effectively predict an early treatment response on radiological images, when a hug sign angle of 360° was detected.

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.

Perceptions of radiologists on structured reporting for cancer imaging-a survey by the European Society of Oncologic Imaging (ESOI).

OBJECTIVES: To assess radiologists' current use of, and opinions on, structured reporting (SR) in oncologic imaging, and to provide recommendations for a structured report template. MATERIALS AND METHODS: An online survey with 28 questions was sent to European Society of Oncologic Imaging (ESOI) members. The questionnaire had four main parts: (1) participant information, e.g., country, workplace, experience, and current SR use; (2) SR design, e.g., numbers of sections and fields, and template use; (3) clinical impact of SR, e.g., on report quality and length, workload, and communication with clinicians; and (4) preferences for an oncology-focused structured CT report. Data analysis comprised descriptive statistics, chi-square tests, and Spearman correlation coefficients. RESULTS: A total of 200 radiologists from 51 countries completed the survey: 57.0% currently utilized SR (57%), with a lower proportion within than outside of Europe (51.0 vs. 72.7%; p = 0.006). Among SR users, the majority observed markedly increased report quality (62.3%) and easier comparison to previous exams (53.5%), a slightly lower error rate (50.9%), and fewer calls/emails by clinicians (78.9%) due to SR. The perceived impact of SR on communication with clinicians (i.e., frequency of calls/emails) differed with radiologists' experience (p < 0.001), and experience also showed low but significant correlations with communication with clinicians (r = - 0.27, p = 0.003), report quality (r = 0.19, p = 0.043), and error rate (r = - 0.22, p = 0.016). Template use also affected the perceived impact of SR on report quality (p = 0.036). CONCLUSION: Radiologists regard SR in oncologic imaging favorably, with perceived positive effects on report quality, error rate, comparison of serial exams, and communication with clinicians. CLINICAL RELEVANCE STATEMENT: Radiologists believe that structured reporting in oncologic imaging improves report quality, decreases the error rate, and enables better communication with clinicians. Implementation of structured reporting in Europe is currently below the international level and needs society endorsement. KEY POINTS: • The majority of oncologic imaging specialists (57% overall; 51% in Europe) use structured reporting in clinical practice. • The vast majority of oncologic imaging specialists use templates (92.1%), which are typically cancer-specific (76.2%). • Structured reporting is perceived to markedly improve report quality, communication with clinicians, and comparison to prior scans.

TRImodal DEfinitive invasive vagiNal carcinoma Treatment (TRIDENT protocol): how a standardized approach may change prognostic outcomes.

OBJECTIVE: Vaginal carcinoma is a rare malignancy accounting for 1-2% of all gynecological cancers. Surgery has a limited role, while definitive radiotherapy-chemotherapy followed by interventional radiotherapy is considered a valid alternative. The aim of the TRIDENT (TRImodal DEfinitive invasive vagiNal carcinoma Treatment) pilot study was to report the results of a modern standardized trimodal protocol treatment consisting of image guided definitive radiotherapy-chemotherapy followed by image guided interventional radiotherapy in terms of safety and efficacy. METHODS: Between January 2019 and December 2021, we analyzed 21 consecutive patients with primary vaginal cancer who had received radiotherapy-chemotherapy followed by interventional radiotherapy. The primary study endpoint was local control, and secondary endpoints were metastasis free survival, overall survival, and rate and severity of acute and late toxicities. RESULTS: 14 patients had FIGO (International Federation of Gynecology and Obstetrics) stage II, five patients had stage III, and two had stage IVB disease. Median total external beam radiotherapy dose for the tumor was 45 Gy. Median total dose on positive nodes was 60 Gy. Median total dose for interventional radiotherapy was 28 Gy over four high dose rate fractions to achieve between 85 and 95 Gy equivalent dose, in 2 Gy fractions (EQD2)α/ß10, to the high risk clinical target volume, and 60 Gy EQD2α/ß10 to the intermediate risk clinical target volume. All patients received weekly platinum based chemotherapy. Median follow-up was 20 months (range 10-56 months). Two year actuarial local control, metastasis free survival, and overall survival rate were 79.4%, 90.5%, and 79.4%, respectively. In terms of acute toxicity, there were no grade 4 events and only one acute grade (G) 3 toxicity (skin). Only vaginal stenosis (G3) was documented 12 months after therapy due to late toxicity. CONCLUSIONS: In this study, definitive radiotherapy-chemotherapy followed by interventional radiotherapy was a safe and effective treatment modality for primary vaginal cancer.

European Association of Nuclear Medicine Focus 5: Consensus on Molecular Imaging and Theranostics in Prostate Cancer.

BACKGROUND: In prostate cancer (PCa), questions remain on indications for prostate-specific membrane antigen (PSMA) positron emission tomography (PET) imaging and PSMA radioligand therapy, integration of advanced imaging in nomogram-based decision-making, dosimetry, and development of new theranostic applications. OBJECTIVE: We aimed to critically review developments in molecular hybrid imaging and systemic radioligand therapy, to reach a multidisciplinary consensus on the current state of the art in PCa. DESIGN, SETTING, AND PARTICIPANTS: The results of a systematic literature search informed a two-round Delphi process with a panel of 28 PCa experts in medical or radiation oncology, urology, radiology, medical physics, and nuclear medicine. The results were discussed and ratified in a consensus meeting. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Forty-eight statements were scored on a Likert agreement scale and six as ranking options. Agreement statements were analysed using the RAND appropriateness method. Ranking statements were analysed using weighted summed scores. RESULTS AND LIMITATIONS: After two Delphi rounds, there was consensus on 42/48 (87.5%) of the statements. The expert panel recommends PSMA PET to be used for staging the majority of patients with unfavourable intermediate and high risk, and for restaging of suspected recurrent PCa. There was consensus that oligometastatic disease should be defined as up to five metastases, even using advanced imaging modalities. The group agreed that [177Lu]Lu-PSMA should not be administered only after progression to cabazitaxel and that [223Ra]RaCl2 remains a valid therapeutic option in bone-only metastatic castration-resistant PCa. Uncertainty remains on various topics, including the need for concordant findings on both [18F]FDG and PSMA PET prior to [177Lu]Lu-PSMA therapy. CONCLUSIONS: There was a high proportion of agreement among a panel of experts on the use of molecular imaging and theranostics in PCa. Although consensus statements cannot replace high-certainty evidence, these can aid in the interpretation and dissemination of best practice from centres of excellence to the wider clinical community. PATIENT SUMMARY: There are situations when dealing with prostate cancer (PCa) where both the doctors who diagnose and track the disease development and response to treatment, and those who give treatments are unsure about what the best course of action is. Examples include what methods they should use to obtain images of the cancer and what to do when the cancer has returned or spread. We reviewed published research studies and provided a summary to a panel of experts in imaging and treating PCa. We also used the research summary to develop a questionnaire whereby we asked the experts to state whether or not they agreed with a list of statements. We used these results to provide guidance to other health care professionals on how best to image men with PCa and what treatments to give, when, and in what order, based on the information the images provide.

Deep learning-based segmentation of multisite disease in ovarian cancer.

PURPOSE: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. METHODS: A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test. RESULTS: Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10-7, 3 × 10-4, 4 × 10-2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10-3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. CONCLUSION: Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. RELEVANCE STATEMENT: Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. KEY POINTS: • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.

Prediction of the Risk of Malignancy of Adnexal Masses during Pregnancy Comparing Subjective Assessment and Non-Contrast MRI Score (NCMS) in Radiologists with Different Expertise.

Ovarian cancer represents 7% of all cancers in pregnant women. Characterising an ovarian mass during pregnancy is essential to avoid unnecessary treatment and, if treatment is required, to plan it accordingly. Although ultrasonography (US) is the first-line modality to characterise adnexal masses, MRI is indicated when adnexal masses are indeterminate at the US examination. An MRI risk stratification system has been proposed to assign a malignancy probability based on the adnexal lesion's MRI, but features of the scoring system require the administration of intravenous gadolinium-based contrast agents, a method that might have a limited use in pregnant women. The non-contrast MRI score (NCMS) has been used and evaluated in non-pregnant women to characterise adnexal masses indeterminate at the US examination. Therefore, we evaluated the diagnostic accuracy of the NCMS in pregnant women, analysing 20 cases referred to our specialised institution. We also evaluated the diagnostic agreement between two radiologists with different expertise. The two readers classified ovarian masses as benign or malignant using both subjective assessment (SA), based on the interpretive evaluation of imaging findings derived from personal experience, and the NCMS, which includes five categories where 4 and 5 indicate a high probability of a malignant mass. The expert radiologist correctly classified 90% of the diagnoses, using both SA and the NCMS, relying on a sensitivity of 85.7% and a specificity of 92.3%, with a false positive rate of 7.7% and a false negative rate of 14.3%. The non-expert radiologist correctly identified patients at a lower rate, especially using the SA. The analysis of the inter-observer agreement showed a K = 0.47 (95% CI: 0.48-0.94) for the SA (agreement in 71.4% of cases) and a K = 0.8 (95% CI: 0.77-1.00) for the NCMS (agreement in 90% of cases). Although in pregnant patients, non-contrast MRI is used, our results support the use of a quantitative score, i.e., the NCMS, as an accurate tool. This procedure may help less experienced radiologists to reduce the rate of false negatives or positives, especially in centres not specialised in gynaecological imaging, making the MRI interpretation easier and more accurate for radiologists who are not experts in the field, either.

The value of MRI in quantification of parametrial invasion and association with prognosis in locally advanced cervical cancer: the "PLACE" study.

OBJECTIVE: This retrospective observational study aims to evaluate the association between the extent of parametrial invasion (PMI) and disease-free survival (DFS) and cancer-specific survival (CSS) in patients with locally advanced cervical cancer (LACC). MATERIALS AND METHODS: This study included patients with LACC showing parametrial invasion at Magnetic Resonance Imaging (MRI). They were treated with neoadjuvant chemo-radiotherapy (CT/RT) before undergoing radical hysterectomy. The staging MRIs were reviewed retrospectively. Measurements of maximum PMI (PMImax) and parametrial length were taken bilaterally. After that, PMIratio was calculated by dividing PMImax by parametrial length. Analysis was conducted on homogeneous subsets of patients, grouped based on their pathological lymph nodal evaluation (N- and N+). Correlations between PMImax and PMIratio with DFS and CSS were evaluated in both the N- and N+ groups, employing univariable Cox regression analysis. RESULTS: Out of 221 patients, 126 (57%) had non-metastatic lymph nodes (N-), while 95 (43%) had metastatic lymph nodes (N+). The median observation period for all these patients was 73 months (95% confidence interval [CI]: 66-77). The 5-year DFS and CSS probability rates were 75% and 85.7%, respectively, for the N- group and 54.3% and 73.6%, respectively, for the N+ group. A higher PMImax (hazard ratio [HR] = 1.09) and PMIratio (HR = 1.04) correlated with worse overall survival in patients in the N- group (p = 0.025 and p = 0.042). These parameters did not show a significant statistical association in the N+ group. CONCLUSIONS: The degree of PMI evaluated on MRI affects outcome in N- patients with LACC. CLINICAL RELEVANCE STATEMENT: The degree of MRI parametrial invasion affects disease-free survival and cancer-specific survival in patients with the International Federation of Gynecology and Obstetrics (FIGO) stage IIB cervical cancer. This MRI finding can be easily incorporated into routine clinical practice. KEY POINTS: • Visual assessment of parametrial invasion on MRI was not significantly associated with prognosis in locally advanced cervical cancer (LACC). • A greater degree of parametrial invasion is associated with poorer disease-free survival and cancer-specific survival in patients with LACC without metastatic lymph node involvement. • The degree of parametrial invasion at MRI has no correlation with prognosis in LACC with metastatic lymph nodes.

The impact of imputation quality on machine learning classifiers for datasets with missing values.

BACKGROUND: Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier's performance. METHODS: We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data. RESULTS: The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised. CONCLUSIONS: It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable.

Many artificial intelligence (AI) methods aim to classify samples of data into groups, e.g., patients with disease vs. those without. This often requires datasets to be complete, i.e., that all data has been collected for all samples. However, in clinical practice this is often not the case and some data can be missing. One solution is to 'complete' the dataset using a technique called imputation to replace those missing values. However, assessing how well the imputation method performs is challenging. In this work, we demonstrate why people should care about imputation, develop a new method for assessing imputation quality, and demonstrate that if we build AI models on poorly imputed data, the model can give different results to those we would hope for. Our findings may improve the utility and quality of AI models in the clinic.

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer.

High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case.

Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools.

Radiomics systematic review in cervical cancer: gynecological oncologists' perspective.

OBJECTIVE: Radiomics is the process of extracting quantitative features from radiological images, and represents a relatively new field in gynecological cancers. Cervical cancer has been the most studied gynecological tumor for what concerns radiomics analysis. The aim of this study was to report on the clinical applications of radiomics combined and/or compared with clinical-pathological variables in patients with cervical cancer. METHODS: A systematic review of the literature from inception to February 2023 was performed, including studies on cervical cancer analysing a predictive/prognostic radiomics model, which was combined and/or compared with a radiological or a clinical-pathological model. RESULTS: A total of 57 of 334 (17.1%) screened studies met inclusion criteria. The majority of studies used magnetic resonance imaging (MRI), but positron emission tomography (PET)/computed tomography (CT) scan, CT scan, and ultrasound scan also underwent radiomics analysis. In apparent early-stage disease, the majority of studies (16/27, 59.3%) analysed the role of radiomics signature in predicting lymph node metastasis; six (22.2%) investigated the prediction of radiomics to detect lymphovascular space involvement, one (3.7%) investigated depth of stromal infiltration, and one investigated (3.7%) parametrial infiltration. Survival prediction was evaluated both in early-stage and locally advanced settings. No study focused on the application of radiomics in metastatic or recurrent disease. CONCLUSION: Radiomics signatures were predictive of pathological and oncological outcomes, particularly if combined with clinical variables. These may be integrated in a model using different clinical-pathological and translational characteristics, with the aim to tailor and personalize the treatment of each patient with cervical cancer.

Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers.

High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models.Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks.Key points• This review presents studies using multiomics and artificial intelligence in ovarian cancer.• Current literature proves that integrative multiomics outperform models using single data types.• Around 60% of studies used a combination of imaging with clinical data.• The combination of genomics and transcriptomics with imaging data was infrequently used.

Early-Stage HCC Percutaneous Locoregional Management: East versus West Perspectives.

Hepatocellular carcinoma represents an important cause of death worldwide. Early-stage hepatocellular carcinoma patients not suitable for surgery can be treated with a variety of minimally invasive locoregional interventional oncology techniques. Various guidelines in different countries address the treatment of hepatocellular carcinoma, but the actual treatment is usually discussed by a multidisciplinary tumor board in a personalized manner, leading to potential treatment differences based on Western and Eastern perspectives. The aim of this paper is to integrate literature evidence with the eminent experiences collected during a focused session at the Mediterranean Interventional Oncology (MIO) Live Congress 2023.

A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data.

The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the development of machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed by the NHSx, was introduced in 2021. We present an extension to the original cleaning pipeline for the clinical data of the database. It has been adjusted to correct additional systematic inconsistencies in the raw data such as patient sex, oxygen levels and date values. The most important changes will be discussed in this paper, whilst the code and further explanations are made publicly available on GitLab. The suggested cleaning will allow global users to work with more consistent data for the development of machine learning tools without being an expert. In addition, it highlights some of the challenges when working with clinical multi-center data and includes recommendations for similar future initiatives.

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation.

Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human-machine collaboration.