Prediction Nomogram for 68Ga-PSMA-11 PET/CT in different clinical settings of PSA failure after radical treatment for Prostate Cancer

Objective The objective of this study was to develop a clinical nomogram to predict gallium-68 prostate-specific membrane antigen positron emission tomography/computed tomography (68Ga-PSMA-11-PET/CT) positivity in different clinical settings of PSA failure. Materials and methods Seven hundred three (n = 703) prostate cancer (PCa) patients with confirmed PSA failure after radical therapy were enrolled. Patients were stratified according to different clinical settings (first-time biochemical recurrence [BCR]: group 1; BCR after salvage therapy: group 2; biochemical persistence after radical prostatectomy [BCP]: group 3; advanced stage PCa before second-line systemic therapies: group 4). First, we assessed 68Ga-PSMA-11-PET/CT positivity rate. Second, multivariable logistic regression analyses were used to determine predictors of positive scan. Third, regression-based coefficients were used to develop a nomogram predicting positive 68Ga-PSMA-11-PET/CT result and 200 bootstrap resamples were used for internal validation. Fourth, receiver operating characteristic (ROC) analysis was used to identify the most informative nomogram’s derived cut-off. Decision curve analysis (DCA) was implemented to quantify nomogram’s clinical benefit. Results 68Ga-PSMA-11-PET/CT overall positivity rate was 51.2%, while it was 40.3% in group 1, 54% in group 2, 60.5% in group 3, and 86.9% in group 4 (p < 0.001). At multivariable analyses, ISUP grade, PSA, PSA doubling time, and clinical setting were independent predictors of a positive scan (all p ≤ 0.04). A nomogram based on covariates included in the multivariate model demonstrated a bootstrap-corrected accuracy of 82%. The nomogram-derived best cut-off value was 40%. In DCA, the nomogram revealed clinical net benefit of > 10%. Conclusions This novel nomogram proved its good accuracy in predicting a positive scan, with values ≥ 40% providing the most informative cut-off in counselling patients to 68Ga-PSMA-11-PET/CT. This tool might be important as a guide to clinicians in the best use of PSMA-based PET imaging.

Outcome analysis of predictors in locally advanced pancreatic cancer

Guidelines report a wide range of options in locally advanced pancreatic cancer (LAPC): definitive chemotherapy or chemoradiotherapy or the emerging stereotactic body radiotherapy (SBRT) (+/- chemotherapy). On behalf of the AIRO (Italian Association of Radiation Oncology and Clinical Oncology) Gastrointestinal Study Group, we collected retrospective clinical data on 419 LAPC from 15 Italian centers. The study protocol (PAULA-1: Pooled Analysis in Unresectable Locally Advanced pancreatic cancer) was approved by institutional review board of S. Orsola-Malpighi Hospital (201/2015/O/OssN). From this large database we performed tree different studies. The first was a retrospective study about 56 LAPC treated with SBRT at a median biologically equivalent dose of 48 Gy +/- chemotherapy. We demonstrated a statistically significant impact of biologically equivalent dose based on an α/β ratio of 10Gy ≥ 48Gy for local control (LC) (p: 0.045) and overall survival (p: 0.042) in LAPC. The second was a retrospective matched-cohort case-control study comparing SBRT (40 patients) and chemoradiation (40 patients) in LAPC in terms of different endpoints. Our findings suggested an equivalence in terms of most outcomes among the two treatments and an advantage of SBRT in terms of LC (p: 0.017). The third study was a retrospective comparison of definitive chemotherapy, chemoradiotherapy and SBRT (+/- chemotherapy) in terms of different outcomes in LAPC. A predictive model for LC in LAPC was also developed reaching an AUC of 68% (CI 58,7%-77,4%). SBRT treatment emerged as a positive predictive factor for improved LC. Findings deriving from our three studies suggest that SBRT is comparable to standard of care (definitive chemotherapy and chemoradiotherapy) in terms of outcomes. SBRT seems to be an emerging therapeutic option in LAPC significantly improving local control. Furthermore, we have shown the potential of a predictive model for LC. Randomized trials are needed to compare these different therapeutic options in LAPC.

Ultra-hypofractionated stereotactic radiotherapy for prostate cancer in 5 fractions: current evidences and our clinical experience

AIMS: The present is a retrospective evaluation of acute genito-urinary (GU) and gastro-intestinal (GI) toxicity, in addition to biochemical recurrence rate in 57 prostate cancer patients treated at our Institution with ultra-hypofractionated RT (UHRT) schedule. METHODS: From January 2021 to December 2022 we have treated 57 patients with prostate cancer, using an UHRT scheme of 5-fractions every other day for a total dose delivered of 36.25 Gy, according to the PACE-B trial treatment schedule. Good urinary function, assessed by International Prostate Symptom Score (IPSS), were required. The simulation CT scans were acquired in supine position and fused with MRI for CTVs definition for every patient. Each treatment was performed by Accuray's TomoTherapy with daily IGRT. The evaluation of the set-up was very restrictive before daily treatment delivery. RESULTS: According to RTOG toxicity scale, the acute GU toxicity at 3 months from RT, GU toxicity was G0 for 30 patients (52.6%), G1 for 26 (45.6%) and G2 for one only (1.75%); rectal toxicity was G0 for 56 patients (98.25%) and G1 for one only (1.75%). The median follow-up (FU) was 9 months (2-24 months). In the following FU months, we observed progressively lower urinary and rectal toxicity, except for one patient who showed G2 GU toxicity at 12 months. All but one patient had a progressive PSA value decrease. CONCLUSIONS: In our experience, UHRT appears to be safe and well tolerated even without the use of rectal spacer devices. A longer FU is necessary to evaluate late toxicity and disease control rate.

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147).