Prediction of non-resectability using the updated Predictive Index Value model assessed by imaging and surgery in tubo-ovarian cancer: a prospective multicenter ISAAC study.

BACKGROUND: A laparoscopy-based scoring system was developed by Fagotti et al (Fagotti or Predictive Index Value (PIV)score) based on the intraoperative presence or absence of carcinomatosis on predefined sites. Later, the authors updated the PIV score calculated only in the absence of one or both absolute criteria of non-resectability (mesenteric retraction and miliary carcinomatosis of the small bowel) (updated PIV model). OBJECTIVE: The aim was to demonstrate the non-inferiority of ultrasound to other imaging methods (contrast enhanced computed tomography (CT) and whole-body diffusion-weighted (WB DWI)/MRI) in predicting non-resectable tumor (defined as residual disease>1 cm) using the updated PIV model in patients with tubo-ovarian cancer. The agreement between imaging and intraoperative findings as a reference was also calculated. STUDY DESIGN: This was a European prospective multicenter observational study. We included patients with suspected tubo-ovarian carcinoma who underwent preoperative staging and prediction of non-resectability at ultrasound, CT, WB-DWI/MRI and surgical exploration. The predictors of non-resectability were suspicious mesenteric retraction and/or miliary carcinomatosis of the small bowel or if absent, a PIV>8 (updated PIV model). The PIV score ranges from 0 to 12 according to the presence of disease in six predefined intra-abdominal sites (great omentum, liver surface, lesser omentum/stomach/spleen, parietal peritoneum, diaphragms, bowel serosa/mesentery). The reference standard was surgical outcome, in terms of residual disease>1 cm, assessed by laparoscopy and/or laparotomy. The area under the receiver operating characteristic curve (AUC) to assess the performance of the methods in predicting non-resectability was reported. Concordance between index tests at detection of disease at six predefined sites and intraoperative exploration as reference standard was also calculated using Cohen's kappa. RESULTS: The study was between 2018 and 2022 in five European gynecological oncology centers. Data from 242 patients having both mandatory index tests (ultrasound and CT) were analyzed. 145/242 (59.9%) patients had no macroscopic residual tumor after surgery (R0) (5/145 laparoscopy and 140/145 laparotomy) and 17/242 (7.0%) had residual tumor ≤1cm (R1) (laparotomy). In 80/242 patients (33.1%), the residual tumor was >1 cm (R2), 30 of them underwent laparotomy and maximum surgery was carried out and 50/80 underwent laparoscopy and cytoreduction was not feasible in all of them. After excluding 18/242 (7.4%) patients operated on but not eligible for extensive surgery, the predictive performance of three imaging methods was analyzed in 167 women. The AUCs of all methods in discriminating between resectable and non-resectable tumor was 0.80 for ultrasound, 0.76 for CT, 0.71 for WB-DWI/MRI and 0.90 for surgical exploration. Ultrasound had the highest agreement (Cohen's kappa ranging from 0.59 to 0.79) compared to CT and WB-DWI/MRI to assess all parameters included in the updated PIV model. CONCLUSIONS: Ultrasound showed non-inferiority to CT and to WB-DWI/MRI in discriminating between resectable and non-resectable tumor using the updated PIV model. Ultrasound had the best agreement between imaging and intraoperative findings in the assessment of parameters included in the updated PIV model. Ultrasound is an acceptable method to assess abdominal disease and predict non-resectability in patients with tubo-ovarian cancer in the hands of specially trained ultrasound examiners.

Patient satisfaction with ultrasound, whole-body CT and whole-body diffusion-weighted MRI for pre-operative ovarian cancer staging: a multicenter prospective cross-sectional survey.

BACKGROUND: In addition to the diagnostic accuracy of imaging methods, patient-reported satisfaction with imaging methods is important. OBJECTIVE: To report a secondary outcome of the prospective international multicenter Imaging Study in Advanced ovArian Cancer (ISAAC Study), detailing patients' experience with abdomino-pelvic ultrasound, whole-body contrast-enhanced computed tomography (CT), and whole-body diffusion-weighted magnetic resonance imaging (WB-DWI/MRI) for pre-operative ovarian cancer work-up. METHODS: In total, 144 patients with suspected ovarian cancer at four institutions in two countries (Italy, Czech Republic) underwent ultrasound, CT, and WB-DWI/MRI for pre-operative work-up between January 2020 and November 2022. After having undergone all three examinations, the patients filled in a questionnaire evaluating their overall experience and experience in five domains: preparation before the examination, duration of examination, noise during the procedure, radiation load of CT, and surrounding space. Pain perception, examination-related patient-perceived unexpected, unpleasant, or dangerous events ('adverse events'), and preferred method were also noted. RESULTS: Ultrasound was the preferred method by 49% (70/144) of responders, followed by CT (38%, 55/144), and WB-DWI/MRI (13%, 19/144) (p<0.001). The poorest experience in all domains was reported for WB-DWI/MRI, which was also associated with the largest number of patients who reported adverse events (eg, dyspnea). Patients reported higher levels of pain during the ultrasound examination than during CT and WB-DWI/MRI (p<0.001): 78% (112/144) reported no pain or mild pain, 19% (27/144) moderate pain, and 3% (5/144) reported severe pain (pain score >7 of 10) during the ultrasound examination. We did not identify any factors related to patients' preferred method. CONCLUSION: Ultrasound was the imaging method preferred by most patients despite being associated with more pain during the examination in comparison with CT and WB-DWI/MRI. TRIAL REGISTRATION NUMBER: NCT03808792.

Developing a robust two-step machine learning multiclassification pipeline to predict primary site in head and neck carcinoma from lymph nodes.

This study aimed to develop a robust multiclassification pipeline to determine the primary tumor location in patients with head and neck carcinoma of unknown primary using radiomics and machine learning techniques. The dataset included 400 head and neck cancer patients with primary tumor in oropharynx, OPC (n = 162), nasopharynx, NPC (n = 137), oral cavity, OC (n = 63), larynx and hypopharynx, HL (n = 38). Two radiomic-based multiclassification pipelines (P1 and P2) were developed. P1 consisted in a direct identification of the primary sites, whereas P2 was based on a two-step approach: in the first step, the number of classes was reduced by merging the two minority classes which were reclassified in the second step. Diverse correlation thresholds (0.75, 0.80, 0.85), feature selection methods (sequential forwards/backwards selection, sequential floating forward selection, neighborhood component analysis and minimum redundancy maximum relevance), and classification models (neural network, decision tree, naïve Bayes, bagged trees and support vector machine) were assessed. P2 outperformed P1, with the best results obtained with the support vector machine classifier including radiomic and clinical features (accuracies of 75.3 % (HL), 75.4 % (OC), 71.3 % (OPC), 92.9 % (NPC)). These results indicate that the two-step multiclassification pipeline integrating radiomics and clinical information is a promising approach to predict the tumor site of unknown primary.

MRI radiomics in head and neck cancer from reproducibility to combined approaches.

The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.

Cylindrical TGR as early radiological predictor of RLT progression in GEPNETs: a proof of concept.

This study aims to assess the predictive capability of cylindrical Tumor Growth Rate (cTGR) in the prediction of early progression of well-differentiated gastro-entero-pancreatic tumours after Radio Ligand Therapy (RLT), compared to the conventional TGR. Fifty-eight patients were included and three CT scans per patient were collected at baseline, during RLT, and follow-up. RLT response, evaluated at follow-up according to RECIST 1.1, was calculated as a percentage variation of lesion diameters over time (continuous values) and as four different RECIST classes. TGR between baseline and interim CT was computed using both conventional (approximating lesion volume to a sphere) and cylindrical (called cTGR, approximating lesion volume to an elliptical cylinder) formulations. Receiver Operating Characteristic (ROC) curves were employed for Progressive Disease class prediction, revealing that cTGR outperformed conventional TGR (area under the ROC equal to 1.00 and 0.92, respectively). Multivariate analysis confirmed the superiority of cTGR in predicting continuous RLT response, with a higher coefficient for cTGR (1.56) compared to the conventional one (1.45). This study serves as a proof of concept, paving the way for future clinical trials to incorporate cTGR as a valuable tool for assessing RLT response.

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research.

INTRODUCTION: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). METHODS AND OBJECTIVES: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. CONCLUSION: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project.