The LUTADOSE trial: tumour dosimetry after the first administration predicts progression free survival in gastro-entero-pancreatic neuroendocrine tumours (GEP NETs) patients treated with [177Lu]Lu-DOTATATE.

PURPOSE: In Peptide Receptor Radionuclide Therapy (PRRT) with [177Lu]Lu-DOTATATE of gastro-entero-pancreatic neuroendocrine tumours (GEP NETs) a question remains open about the potential benefits of personalised dosimetry. This observational prospective study examines the association of individualized dosimetry with progression free survival (PFS) in G1-G2 GEP NETs patients following the standard [177Lu]Lu-DOTATATE therapeutic regimen. METHODS: The analysis was conducted on 42 patients administered 4 times, and on 165 lesions. Dosimetry was performed after the first and the forth cycle, with two SPECT/CT scans at day 1 and 7 after administration. Global mean Tumour absorbed Dose of each patient (GTD) was calculated after cycle 1 and 4 as the sum of lesion doses weighted by lesion mass, normalized by the global tumour mass. Cumulative GTD_TOT was calculated as the mean between cycle 1 (GTD_1) and 4 (GTD_4) multiplied by 4. Patients were followed-up for median 32.8 (range 18-45.5) months, through blood tests and contrast enhanced CT (ceCT). This study assessed the correlation between global tumour dose (GTD) and PFS longer or shorter than 24 months. After a ROC analysis, we stratified patients according to the best cut-off value for two additional statistical analyses. At last a multivariate analysis was carried out for PFS > / < 24 months. RESULTS: The median follow-up interval was 33 months, ranging from 18 to 45.5 months. The median PFS was 42 months. The progression free survival rate at 20 months was 90.5%. GTD_1 and GTD_TOT were statistically associated with PFS > / < 24 m (p = 0.026 and p = 0.03 respectively). The stratification of patients on GTD_1 lower or higher than the best cut-off value at 10.6 Gy provided significantly different median PFS of 21 months versus non reached, i.e. longer than 45.5 months (p = 0.004), with a hazard ratio of 8.6, (95% C.I.: [2 - 37]). Using GTD_TOT with the best cut-off at 43 Gy, the same PFS values were obtained as after cycle 1 (p = 0.035). At multivariate analysis, a decrease in GTD_1 and, with lower impact, a higher global tumour volume were significantly associated with PFS < 24 months. We calculated the Tumour Control Probability of obtaining PFS > 24 months as a function of GTD_1. DISCUSSION: Several statistical analyses seem to confirm that simple tumour dosimetry with 2 SPECT/CT scans after the first administration allows to predict PFS values after 4 × 7.4 GBq administrations of 177Lu[Lu]-DOTATATE in G1-G2 GEP NETs. This result qualitatively confirms recent findings by a Belgian and a French study. However, dosimetric thresholds are different. This probably comes from different cohort baseline characteristics, since the median PFS in our study (42 m) was longer than in the other studies (28 m and 31 m). CONCLUSION: Tumour dosimetry after the first administration of [177Lu]Lu-DOTATATE offers an important prognostic value in the clinical decision-making process, especially for the future as alternative emitters or administration schedule may become available.

Personalised PET imaging in oncology: an umbrella review of meta-analyses to guide the appropriate radiopharmaceutical choice and indication.

PURPOSE: For several years, oncological positron emission tomography (PET) has developed beyond 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG). This umbrella review of meta-analyses aims to provide up-to-date, comprehensive, high-level evidence to support appropriate referral for a specific radiopharmaceutical PET/computed tomography (CT) or PET/magnetic resonance (MR) in the diagnosis and staging of solid cancers other than brain malignancies. METHODS: We performed a systematic literature search on the PubMed/MEDLINE and EMBASE databases for meta-analyses assessing the accuracy of PET/CT and/or PET/MRI with [18F]FDG, somatostatin- receptor-targeting 68Ga-DOTA-peptides, 18F-labelled dihydroxyphenylalanine ([18F]DOPA), prostate-specific membrane antigen (PSMA)-targeted radioligands, and fibroblast activation protein inhibitors (FAPI) in the diagnosis/disease characterisation and staging of solid cancers other than brain tumours. RESULTS: The literature search yielded 449 scientific articles. After screening titles and abstracts and applying inclusion and exclusion criteria, we selected 173 meta-analyses to assess the strength of evidence. One article was selected from references. Sixty-four meta-analyses were finally considered. The current evidence corroborates the role of [18F]FDG as the main player in molecular imaging; PSMA tracers are useful in staging and re-staging prostate cancer; somatostatin-targeting peptides (e.g. [68Ga]Ga- DOTA-TOC and -TATE) or [18F]DOPA are valuable in neuroendocrine tumours (NETs). FAPI has emerged in gastric cancer assessment. According to search and selection criteria, no satisfactory meta-analysis was selected for the diagnosis/detection of oesophageal cancer, the diagnosis/detection and N staging of small cell lung cancer and hepatic cell carcinoma, the diagnosis/detection and M staging of melanoma and Merkel cell carcinoma, cervical, vulvar and penis cancers, the N and M staging of lung and gastroenteropancreatic NET, testicular cancer, and chondrosarcoma, and the M staging of differentiated thyroid, bladder and anal cancers. CONCLUSION: The comprehensive high-level evidence synthesised in the present umbrella review serves as a guiding compass for clinicians and imagers, aiding them in navigating the increasingly intricate seascape of PET examinations.

FAP expression in alpha cells of Langherhans insulae-implications for FAPI radiopharmaceuticals' use.

PURPOSE: Radiopharmaceuticals targeting fibroblast activation protein (FAP) alpha are increasingly studied for diagnostic and therapeutic applications. We discovered FAP expression at immunohistochemistry (IHC) in the alpha cells of the Langerhans insulae of few patients. Therefore, we planned an investigation aimed at describing FAP expression in the pancreas and discussing the implications for radioligand applications. METHODS: We retrospectively included 40 patients from 2 institutions (20 pts each) according to the following inclusion/exclusion criteria: (i) pathology proven pancreatic ductal adenocarcinoma and neuroendocrine tumors (NET), 10 pts per each group at each center; (ii) and availability of paraffin-embedded tissue; and (iii) clinical-pathological records. We performed IHC analysis and applied a semiquantitative visual scoring system (0, negative staining; 1, present in less than 30%; 2, present in more than 30% of the area). FAP expression was assessed according to histology-NET (n = 20) vs ductal adenocarcinoma (n = 20)-and to previous treatments within the adenocarcinoma group. The local ethics committee approved the study (No. INT 21/16, 28 January 2016). RESULTS: The population consisted of 24 males and 16 females, with a median age of 68 and a range of 14-84 years; 8/20 adenocarcinoma patients received chemotherapy. In all the Langerhans insulae (40/40), pancreatic alpha cells were found to express FAP, with a score of 2. No difference was found among NET (20/20) and adenocarcinoma (20/20), nor according to neoadjuvant chemotherapy in the adenocarcinoma cohort (received or not received). CONCLUSION: Pancreatic Langerhans islet alpha cells normally express FAP. This is not expected to influence the diagnostic accuracy of FAP-targeting tracers. In the therapeutic setting, our results suggest the need to better elucidate FAPI radioligands' effects on the Langerhans insulae function.

PET/CT radiomics in breast cancer: Mind the step.

The aim of the present review was to assess the current status of positron emission tomography/computed tomography (PET/CT) radiomics research in breast cancer, and in particular to analyze the strengths and weaknesses of the published papers in order to identify challenges and suggest possible solutions and future research directions. Various combinations of the terms "breast", "radiomic", "PET", "radiomics", "texture", and "textural" were used for the literature search, extended until 8 July 2019, within the PubMed/MEDLINE database. Twenty-six articles fulfilling the inclusion/exclusion criteria were retrieved in full text and analyzed. The studies had technical and clinical objectives, including diagnosis, biological characterization (correlation with histology, molecular subtypes and IHC marker expression), prediction of response to neoadjuvant chemotherapy, staging, and outcome prediction. We reviewed and discussed the selected investigations following the radiomics workflow steps related to the clinical, technical, analysis, and reporting issues. Most of the current evidence on the clinical role of PET/CT radiomics in breast cancer is at the feasibility level. Harmonized methods in image acquisition, post-processing and features calculation, predictive models and classifiers trained and validated on sufficiently representative datasets, adherence to consensus guidelines, and transparent reporting will give validity and generalizability to the results.

Climbing the steps of the evidence-based medicine pyramid: highlights from Annals of Nuclear Medicine 2019.

We aimed to provide an overview on research path in nuclear medicine climbing the steps of the Evidence-Based Medicine (EBM) pyramid using review of 14 subjectively selected papers out of 111 published in the Annals of Nuclear Medicine during January-December 2019. Following the structure of the EBM hierarchy, we chose at least one study for each step of the pyramid from the basis (pre-clinical research, expert opinion, case report and case series), to the middle (case-control and cohort studies, randomised controlled trials), towards the top (meta-analyses and systematic reviews). Additionally, we collected information on the promoter of each included study: investigator-initiated trials (IITs) vs industry-sponsored trials (ISTs). We found that pre-clinical studies are primarily focused on the development of novel molecular targets in cancer, with promising results. At the same time, clinical investigations deal with cardiological, neurological, infectious and oncological applications using both SPECT and PET modalities. Additionally, radionuclide therapy gained interest and is experiencing comprehensive clinical implementation. Our overview confirms the current central role of IITs as compared with ISTs. Challenges and future directions in Nuclear Medicine research are discussed.

State-of-the-art of FAPI-PET imaging: a systematic review and meta-analysis.

INTRODUCTION: Fibroblast activation protein-α (FAPα) is overexpressed on cancer-associated fibroblasts in approximately 90% of epithelial neoplasms, representing an appealing target for therapeutic and molecular imaging applications. [68 Ga]Ga-labelled radiopharmaceuticals-FAP-inhibitors (FAPI)-have been developed for PET. We systematically reviewed and meta-analysed published literature to provide an overview of its clinical role. MATERIALS AND METHODS: The search, limited to January 1st, 2018-March 31st, 2021, was performed on MedLine and Embase databases using all the possible combinations of terms "FAP", "FAPI", "PET/CT", "positron emission tomography", "fibroblast", "cancer-associated fibroblasts", "CAF", "molecular imaging", and "fibroblast imaging". Study quality was assessed using the QUADAS-2 criteria. Patient-based and lesion-based pooled sensitivities/specificities of FAPI PET were computed using a random-effects model directly from the STATA "metaprop" command. Between-study statistical heterogeneity was tested (I2-statistics). RESULTS: Twenty-three studies were selected for systematic review. Investigations on staging or restaging head and neck cancer (n = 2, 29 patients), abdominal malignancies (n = 6, 171 patients), various cancers (n = 2, 143 patients), and radiation treatment planning (n = 4, 56 patients) were included in the meta-analysis. On patient-based analysis, pooled sensitivity was 0.99 (95% CI 0.97-1.00) with negligible heterogeneity; pooled specificity was 0.87 (95% CI 0.62-1.00), with negligible heterogeneity. On lesion-based analysis, sensitivity and specificity had high heterogeneity (I2 = 88.56% and I2 = 97.20%, respectively). Pooled sensitivity for the primary tumour was 1.00 (95% CI 0.98-1.00) with negligible heterogeneity. Pooled sensitivity/specificity of nodal metastases had high heterogeneity (I2 = 89.18% and I2 = 95.74%, respectively). Pooled sensitivity in distant metastases was good (0.93 with 95% CI 0.88-0.97) with negligible heterogeneity. CONCLUSIONS: FAPI-PET appears promising, especially in imaging cancers unsuitable for [18F]FDG imaging, particularly primary lesions and distant metastases. However, high-level evidence is needed to define its role, specifically to identify cancer types, non-oncological diseases, and clinical settings for its applications.

Distributed learning: a reliable privacy-preserving strategy to change multicenter collaborations using AI.

PURPOSE: The present scoping review aims to assess the non-inferiority of distributed learning over centrally and locally trained machine learning (ML) models in medical applications. METHODS: We performed a literature search using the term "distributed learning" OR "federated learning" in the PubMed/MEDLINE and EMBASE databases. No start date limit was used, and the search was extended until July 21, 2020. We excluded articles outside the field of interest; guidelines or expert opinion, review articles and meta-analyses, editorials, letters or commentaries, and conference abstracts; articles not in the English language; and studies not using medical data. Selected studies were classified and analysed according to their aim(s). RESULTS: We included 26 papers aimed at predicting one or more outcomes: namely risk, diagnosis, prognosis, and treatment side effect/adverse drug reaction. Distributed learning was compared to centralized or localized training in 21/26 and 14/26 selected papers, respectively. Regardless of the aim, the type of input, the method, and the classifier, distributed learning performed close to centralized training, but two experiments focused on diagnosis. In all but 2 cases, distributed learning outperformed locally trained models. CONCLUSION: Distributed learning resulted in a reliable strategy for model development; indeed, it performed equally to models trained on centralized datasets. Sensitive data can get preserved since they are not shared for model development. Distributed learning constitutes a promising solution for ML-based research and practice since large, diverse datasets are crucial for success.

Increased incidence of interstitial pneumonia detected on [18F]-FDG-PET/CT in asymptomatic cancer patients during COVID-19 pandemic in Lombardy: a casualty or COVID-19 infection?

PURPOSE: The study aimed to compare the incidence of interstitial pneumonia on [18F]-FDG PET/CT scans between two 6-month periods: (a) the COVID-19 pandemic peak and (b) control period. Secondly, we compared the incidence of interstitial pneumonia on [18F]-FDG PET/CT and epidemiological data from the regional registry of COVID-19 cases. Additionally, imaging findings and the intensity of [18F]-FDG PET/CT uptake in terms of maximum standardized uptake value (SUVmax) were compared. METHODS: We retrospectively analyzed [18F]-FDG PET/CT scans performed in cancer patients referred to nuclear medicine of Humanitas Gavazzeni in Bergamo from December 2019 to May 2020 and from December 2018 to May 2019. The per month incidence of interstitial pneumonia at imaging and the epidemiological data were assessed. To evaluate the differences between the two symmetric groups (period of COVID-19 pandemic and control), the stratified Cochran-Mantel-Haenszel test was used. Chi-square test or Fisher's exact test and t test or Wilcoxon test were performed to compare the distributions of categorical and continuous variables, respectively. RESULTS: Overall, 1298 patients were included in the study. The two cohorts-COVID-19 pandemic (n = 575) and control (n = 723)-did not statistically differ in terms of age, disease, or scan indication (p > 0.05). Signs of interstitial pneumonia were observed in 24 (4.2%) and 14 patients (1.9%) in the COVID-19 period and the control period, respectively, with a statistically significant difference (p = 0.013). The level of statistical significance improved further when the period from January to May was considered, with a peak in March (7/83 patients, 8.4% vs 3/134 patients, 2.2%, p = 0.001). The curve of interstitial pneumonia diagnosis overlapped with the COVID-19 incidence in the area of Lombardy (Spearman correlation index was equal to 1). Imaging data did not differ among the two cohorts. CONCLUSIONS: Significant increase of interstitial lung alterations at [18F]-FDG PET/CT has been demonstrated during the COVID-19 pandemic. Additionally, the incidence curve of imaging abnormalities resulted in resembling the epidemiological data of the general population. These data support the rationale to adopt [18F]-FDG PET/CT as sentinel modality to identify suspicious COVID-19 cases to be referred for additional confirmatory testing. Nuclear medicine physicians and staff should continue active surveillance of interstitial pneumonia findings, especially when new infection peak is expected.

Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer.

OBJECTIVE: The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC). METHODS: In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence. RESULTS: Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87. CONCLUSIONS: Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.

FDG-PET/CT findings highly suspicious for COVID-19 in an Italian case series of asymptomatic patients.

AIM: To illustrate the [18F]FDG-PET/CT findings in patients affected by cancer with clinical diagnosis of Covid-19 METHODS: We retrospectively reviewed the cases of patients who showed pulmonary involvement unrelated to cancer metastases on March 13 and 16 2020. We reviewed the scans, collected medical history, and exposure information. RESULTS: Among the 13 scans, we identified 5 cases with imaging findings suspicious for viral infection. Peripheral lung consolidations and/or ground-glass opacities in two or more lobes were found. Lung abnormalities displayed increased [18F]FDG uptake (SUVmax 4.3-11.3). All the patients on the day of PET/CT acquisition were asymptomatic, and they did not have fever or cough. In view of the PET/CT findings, home isolation, symptom surveillance, and treatment (in 3/5 patients) were indicated. At 1-week follow-up, 2/5 patients experienced the onset of mild respiratory symptoms. CONCLUSIONS: The [18F]FDG-PET/CT can identify probable Covid-19 disease in the absence or before symptoms onset and can guide patient management. Nuclear medicine staff needs to be aware of the possibility of contact with patients affected by the SARS-CoV-2 infection even if they do not present any symptom. Therefore, safety measures need to be adopted for other patients and hospital staff in order to block the spread of infection.

Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study.

OBJECTIVE: To investigate whether pretreatment MRI-based radiomics of locally advanced rectal cancer (LARC) and/or the surrounding mesorectal compartment (MC) can predict pathologic complete response (pCR), neoadjuvant rectal (NAR) score, and tumor regression grade (TRG). METHODS: One hundred thirty-two consecutive patients with LARC who underwent neoadjuvant chemoradiation and total mesorectal excision (TME) were retrospectively collected from 2 centers in the USA and Italy. The primary tumor and surrounding MC were segmented on the best available T2-weighted sequence (axial, coronal, or sagittal). Three thousand one hundred ninety radiomic features were extracted using a python package. The most salient radiomic features as well as MRI parameter and clinical-based features were selected using recursive feature elimination. A logistic regression classifier was built to distinguish between any 2 binned categories in the considered endpoints: pCR, NAR, and TRG. Repeated k-fold validation was performed and AUCs calculated. RESULTS: There were 24, 87, and 21 T4, T3, and T2 LARCs, respectively (median age 63 years, 32 to 86). For NAR and TRG, the best classification performance was obtained using both the tumor and MC segmentations. The AUCs for classifying NAR 0 versus 2, pCR, and TRG 0/1 versus 2/3 were 0.66 (95% CI, 0.60-0.71), 0.80 (95% CI, 0.74-0.85), and 0.80 (95% CI, 0.77-0.82), respectively. CONCLUSION: Radiomics of pretreatment MRIs can predict pCR, TRG, and NAR score in patients with LARC undergoing neoadjuvant treatment and TME with moderate accuracy despite extremely heterogenous image data. Both the tumor and MC contain important prognostic information. KEY POINTS: • Machine learning of rectal cancer on images from the pretreatment MRI can predict important patient outcomes with moderate accuracy. • The tumor and the tissue around it both contain important prognostic information.

Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas.

OBJECTIVES: We aimed to assess the ability of radiomics, applied to not-enhanced computed tomography (CT), to differentiate mediastinal masses as thymic neoplasms vs lymphomas. METHODS: The present study was an observational retrospective trial. Inclusion criteria were pathology-proven thymic neoplasia or lymphoma with mediastinal localization, availability of CT. Exclusion criteria were age < 16 years and mediastinal lymphoma lesion < 4 cm. We selected 108 patients (M:F = 47:61, median age 48 years, range 17-79) and divided them into a training and a validation group. Radiomic features were used as predictors in linear discriminant analysis. We built different radiomic models considering segmentation software and resampling setting. Clinical variables were used as predictors to build a clinical model. Scoring metrics included sensitivity, specificity, accuracy and area under the curve (AUC). Wilcoxon paired test was used to compare the AUCs. RESULTS: Fifty-five patients were affected by thymic neoplasia and 53 by lymphoma. In the validation analysis, the best radiomics model sensitivity, specificity, accuracy and AUC resulted 76.2 ± 7.0, 77.8 ± 5.5, 76.9 ± 6.0 and 0.84 ± 0.06, respectively. In the validation analysis of the clinical model, the same metrics resulted 95.2 ± 7.0, 88.9 ± 8.9, 92.3 ± 8.5 and 0.98 ± 0.07, respectively. The AUCs of the best radiomic and the clinical model not differed. CONCLUSIONS: We developed and validated a CT-based radiomic model able to differentiate mediastinal masses on non-contrast-enhanced images, as thymic neoplasms or lymphoma. The proposed method was not affected by image postprocessing. Therefore, the present image-derived method has the potential to noninvasively support diagnosis in patients with prevascular mediastinal masses with major impact on management of asymptomatic cases.

Quantitative imaging biomarkers in nuclear medicine: from SUV to image mining studies. Highlights from annals of nuclear medicine 2018.

OBJECTIVE: Quantification in medical imaging is one of the main goals in research and clinical practice since it allows immediate understanding, objective communication, and comparison. Our aim was to summarize relevant investigations on quantification in nuclear medicine studies published in the volume 32 of Annals of Nuclear Medicine. METHODS: In this article, we summarized the data of 14 selected papers from international research groups that were published between January and December 2018. This is a descriptive review with an inherently subjective selection of articles. RESULTS: We discussed the role of parameters ranging from standardized uptake value to ratios, to flow within a region of interest, to volumetric parameters and to texture indices in different clinical scenarios in oncology, cardiology, and neurology. CONCLUSIONS: In all the medical disciplines in which nuclear medicine examinations play a role, quantification is essential both in research and in clinical practice. Standardization and high-quality protocols are crucial for the success and reliability of imaging biomarkers.

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.

PURPOSE: The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process. METHODS: Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies. RESULTS: Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials. CONCLUSIONS: The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.

PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy.

PURPOSE: To assess the role of radiomics parameters in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer. METHODS: Seventy-nine patients who had undergone pretreatment staging 18F-FDG PET/CT and treatment with NAC between January 2010 and January 2018 were included in the study. Primary lesions on PET images were delineated, and extraction of first-, second-, and higher-order imaging features was performed using LIFEx software. The relationship between these parameters and pCR to NAC was analyzed by multiple logistic regression models. RESULTS: Nineteen patients (24%) had pCR to NAC. Different models were generated on complete information and imputed datasets, using univariable and multivariable logistic regression and least absolute shrinkage and selection operator (lasso) regression. All models could predict pCR to NAC, with area under the curve values ranging from 0.70 to 0.73. All models agreed that tumor molecular subtype is the primary predictor of the primary endpoint. CONCLUSIONS: Our models predicted that patients with subtype 2 and subtype 3 (HER2+ and triple negative, respectively) are more likely to have a pCR to NAC than those with subtype 1 (luminal). The association between PET imaging features and pCR suggested that PET imaging features could be considered as potential predictors of pCR in locally advanced breast cancer patients.

Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions.

PURPOSE: To evaluate the ability of CT and PET radiomics features to classify lung lesions as primary or metastatic, and secondly to differentiate histological subtypes of primary lung cancers. METHODS: A cohort of 534 patients with lung lesions were retrospectively studied. Radiomics texture features were extracted using the LIFEx package from semiautomatically segmented PET and CT images. Histology data were recorded in all patients. The patient cohort was divided into a training and a validation group and linear discriminant analysis (LDA) was performed to classify the lesions using both direct and backward stepwise methods. The robustness of the procedure was tested by repeating the entire process 100 times with different assignments to the training and validation groups. Scoring metrics included analysis of the receiver operating characteristic curves in terms of area under the curve (AUC), sensitivity, specificity and accuracy. RESULTS: Radiomics features extracted from CT and PET datasets were able to differentiate primary tumours from metastases in both the training and the validation group (AUCs 0.79 ± 0.03 and 0.70 ± 0.04, respectively, from the CT dataset; AUCs 0.92 ± 0.01 and 0.91 ± 0.03, respectively, from the PET dataset). The AUC cut-off thresholds identified by LDA using direct and backward elimination strategies were -0.79 ± 0.06 and -0.81 ± 0.08, respectively (CT dataset) and -0.69 ± 0.05 and -0.68 ± 0.04, respectively (PET dataset). For differentiation between primary subgroups based on CT features, the AUCs in the training and validation groups were 0.81 ± 0.02 and 0.69 ± 0.04 for adenocarcinoma (Adc) vs. squamous cell carcinoma (Sqc) or "Other", 0.85 ± 0.02 and 0.70 ± 0.05 for Sqc vs. Adc or Other, and 0.77 ± 0.03 and 0.57 ± 0.05 for Other vs. Adc or Sqc. The same analyses for the PET data revealed AUCs of 0.90 ± 0.10 and 0.80 ± 0.04, 0.80 ± 0.02 and 0.61 ± 0.06, and 0.97 ± 0.01 and 0.88 ± 0.04, respectively. CONCLUSION: PET radiomics features were able to differentiate between primary and metastatic lung lesions and showed the potential to identify primary lung cancer subtypes.

Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery.

PURPOSE: Radiomic features derived from the texture analysis of different imaging modalities e show promise in lesion characterisation, response prediction, and prognostication in lung cancer patients. The present study aimed to identify an images-based radiomic signature capable of predicting disease-free survival (DFS) in non-small cell lung cancer (NSCLC) patients undergoing surgery. METHODS: A cohort of 295 patients was selected. Clinical parameters (age, sex, histological type, tumour grade, and stage) were recorded for all patients. The endpoint of this study was DFS. Both computed tomography (CT) and fluorodeoxyglucose positron emission tomography (PET) images generated from the PET/CT scanner were analysed. Textural features were calculated using the LifeX package. Statistical analysis was performed using the R platform. The datasets were separated into two cohorts by random selection to perform training and validation of the statistical models. Predictors were fed into a multivariate Cox proportional hazard regression model and the receiver operating characteristic (ROC) curve as well as the corresponding area under the curve (AUC) were computed for each model built. RESULTS: The Cox models that included radiomic features for the CT, the PET, and the PET+CT images resulted in an AUC of 0.75 (95%CI: 0.65-0.85), 0.68 (95%CI: 0.57-0.80), and 0.68 (95%CI: 0.58-0.74), respectively. The addition of clinical predictors to the Cox models resulted in an AUC of 0.61 (95%CI: 0.51-0.69), 0.64 (95%CI: 0.53-0.75), and 0.65 (95%CI: 0.50-0.72) for the CT, the PET, and the PET+CT images, respectively. CONCLUSIONS: A radiomic signature, for either CT, PET, or PET/CT images, has been identified and validated for the prediction of disease-free survival in patients with non-small cell lung cancer treated by surgery.

[18F]FDG PET/CT features for the molecular characterization of primary breast tumors.

PURPOSE: The aim of this study was to evaluate the role of imaging features derived from [18F]FDG-PET/CT to provide in vivo characterization of breast cancer (BC). METHODS: Images from 43 patients with a first diagnosis of BC were reviewed. Images were acquired before any treatment. Histological data were derived from pretreatment biopsy or surgical histological specimen; these included tumor type, grade, ER and PgR receptor status, lymphovascular invasion, Ki67 index, HER2 status, and molecular subtype. Standard parameters (SUVmean, TLG, MTV) and advanced imaging features (histogram-based and shape and size features) were evaluated. Univariate analysis, hierarchical clustering analysis, and exact Fisher's test were used for statistical analysis of data. Imaging-derived metrics were reduced evaluating the mutual correlation within group of features as well as the mutual correlation between groups of features to form a signature. RESULTS: A significant correlation was found between some advanced imaging features and the histological type. Different molecular subtypes were characterized by different values of two histogram-based features (median and energy). A significant association was observed between the imaging signature and luminal A and luminal B HER2 negative molecular subtype and also when considering luminal A, luminal B HER2-negative and HER2-positive groups. Similar results were found between the signature and all five molecular subtypes and also when considering the histological types of BC. CONCLUSIONS: Our results suggest a complementary role of standard PET imaging parameters and advanced imaging features for the in vivo biological characterization of BC lesions.

Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy.

BACKGROUND: To appraise the ability of a radiomics based analysis to predict local response and overall survival for patients with hepatocellular carcinoma. METHODS: A set of 138 consecutive patients (112 males and 26 females, median age 66 years) presented with Barcelona Clinic Liver Cancer (BCLC) stage A to C were retrospectively studied. For a subset of these patients (106) complete information about treatment outcome, namely local control, was available. Radiomic features were computed for the clinical target volume. A total of 35 features were extracted and analyzed. Univariate analysis was used to identify clinical and radiomics significant features. Multivariate models by Cox-regression hazards model were built for local control and survival outcome. Models were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC) curve. For the LC analysis, two models selecting two groups of uncorrelated features were analyzes while one single model was built for the OS analysis. RESULTS: The univariate analysis lead to the identification of 15 significant radiomics features but the analysis of cross correlation showed several cross related covariates. The un-correlated variables were used to build two separate models; both resulted into a single significant radiomic covariate: model-1: energy p < 0.05, AUC of ROC 0.6659, C.I.: 0.5585-0.7732; model-2: GLNU p < 0.05, AUC 0.6396, C.I.:0.5266-0.7526. The univariate analysis for covariates significant with respect to local control resulted in 9 clinical and 13 radiomics features with multiple and complex cross-correlations. After elastic net regularization, the most significant covariates were compacity and BCLC stage, with only compacity significant to Cox model fitting (Cox model likelihood ratio test p < 0.0001, compacity p < 0.00001; AUC of the model is 0.8014 (C.I. = 0.7232-0.8797)). CONCLUSION: A robust radiomic signature, made by one single feature was finally identified. A validation phases, based on independent set of patients is scheduled to be performed to confirm the results.

Toxicity and efficacy of salvage carbon 11-choline positron emission tomography/computed tomography-guided radiation therapy in patients with lymph node recurrence of prostate cancer.

OBJECTIVE: To report the 3-year toxicity and outcomes of carbon 11 (11C)-choline-positron emission tomography (PET)/computed tomography (CT)-guided radiotherapy (RT), delivered via helical tomotherapy (HTT; Tomotherapy® Hi-Art II® Treatment System, Accuray Inc., Sunnyvale, CA, USA) after lymph node (LN) relapses in patients with prostate cancer. PATIENTS AND METHODS: From January 2005 to March 2013, 81 patients with biochemical recurrence after surgery, with or without adjuvant/salvage RT or radical RT, and with evidence of LN 11C-choline-PET/CT pathological uptake, underwent HTT (median [range] prostate-specific antigen level 2.59 [0.61-187] ng/mL). Of the 81 patients, 72 were treated at the pelvic and/or lumbar-aortic LN chain with HTT at 51.8 Gy/28 fr and with simultaneous integrated boost to a median dose of 65.5 Gy on the pathological uptake sites detected by 11C-choline-PET/CT. Nine patients were treated without simultaneous integrated boost (50-65.5 Gy, 25-30 fr). RESULTS: With a median (range) follow-up of 36 (9-116) months, 91.4% of the patients had a PSA reduction 3 months after HTT. The 3-year overall, local relapse-free and clinical relapse-free survival rates were 80.0, 89.8 and 61.8%, respectively. The 3-year actuarial incidences of ≥grade 2 rectal and ≥grade 2 genitourinary toxicity were 6.6% (±2.9%) and 26.3% (±5.5%), respectively. A PSA nadir of ≥0.26 ng/mL (hazard ratio [HR] 3.6, 95% confidence interval [CI] 1.7-7.7; P = 0.001), extrapelvic 11C-choline-PET/CT-positive LN location (HR 2.4, 95% CI 0.9-6.4; P = 0.07), RT previous to HTT (HR 2.7; 95% CI 1.07-6.9, P = 0.04) and number of positive LNs (HR 1.13, 95% CI 1.04-1.22; P = 0.003) were the main predictors of clinical relapse after HTT. CONCLUSIONS: 11C-choline-PET/CT-guided HTT is safe and effective in the treatment of LN relapses of prostate cancer in previously treated patients.