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The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.
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AIM: The optimal management for early recurrent prostate cancer following radical prostatectomy (RP) in patients with negative prostate-specific membrane antigen positron-emission tomography (PSMA-PET) scan is an ongoing subject of debate. The aim of this study was to evaluate the outcome of salvage radiotherapy (SRT) in patients with biochemical recurrence with negative PSMA PET finding. METHODS: This retrospective, multicenter (11 centers, 5 countries) analysis included patients who underwent SRT following biochemical recurrence (BR) of PC after RP without evidence of disease on PSMA-PET staging. Biochemical recurrence-free survival (bRFS), metastatic-free survival (MFS) and overall survival (OS) were assessed using Kaplan-Meier method. Multivariable Cox proportional hazards regression assessed predefined predictors of survival outcomes. RESULTS: Three hundred patients were included, 253 (84.3%) received SRT to the prostate bed only, 46 (15.3%) additional elective pelvic nodal irradiation, respectively. Only 41 patients (13.7%) received concomitant androgen deprivation therapy (ADT). Median follow-up after SRT was 33 months (IQR: 20-46 months). Three-year bRFS, MFS, and OS following SRT were 73.9%, 87.8%, and 99.1%, respectively. Three-year bRFS was 77.5% and 48.3% for patients with PSA levels before PSMA-PET ≤ 0.5 ng/ml and > 0.5 ng/ml, respectively. Using univariate analysis, the International Society of Urological Pathology (ISUP) grade > 2 (p = 0.006), metastatic pelvic lymph nodes at surgery (p = 0.032), seminal vesicle involvement (p < 0.001), pre-SRT PSA level of > 0.5 ng/ml (p = 0.004), and lack of concomitant ADT (p = 0.023) were significantly associated with worse bRFS. On multivariate Cox proportional hazards, seminal vesicle infiltration (p = 0.007), ISUP score >2 (p = 0.048), and pre SRT PSA level > 0.5 ng/ml (p = 0.013) remained significantly associated with worse bRFS. CONCLUSION: Favorable bRFS after SRT in patients with BR and negative PSMA-PET following RP was achieved. These data support the usage of early SRT for patients with negative PSMA-PET findings.
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Próstata , Neoplasias da Próstata , Masculino , Humanos , Próstata/patologia , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/radioterapia , Neoplasias da Próstata/cirurgia , Prognóstico , Antígeno Prostático Específico , Glândulas Seminais/patologia , Estudos Retrospectivos , Antagonistas de Androgênios , Recidiva Local de Neoplasia/patologia , Prostatectomia , Tomografia por Emissão de Pósitrons , Terapia de Salvação , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/métodosRESUMO
PURPOSE: Despite growing evidence for bilateral pelvic radiotherapy (whole pelvis RT, WPRT) there is almost no data on unilateral RT (hemi pelvis RT, HPRT) in patients with nodal recurrent prostate cancer after prostatectomy. Nevertheless, in clinical practice HPRT is sometimes used with the intention to reduce side effects compared to WPRT. Prostate-specific membrane antigen positron emission tomography / computed tomography (PSMA-PET/CT) is currently the best imaging modality in this clinical situation. This analysis compares PSMA-PET/CT based WPRT and HPRT. METHODS: A propensity score matching was performed in a multi-institutional retrospective dataset of 273 patients treated with pelvic RT due to nodal recurrence (214 WPRT, 59 HPRT). In total, 102 patients (51 in each group) were included in the final analysis. Biochemical recurrence-free survival (BRFS) defined as prostate specific antigen (PSA) < post-RT nadir + 0.2ng/ml, metastasis-free survival (MFS) and nodal recurrence-free survival (NRFS) were calculated using the Kaplan-Meier method and compared using the log rank test. RESULTS: Median follow-up was 29 months. After propensity matching, both groups were mostly well balanced. However, in the WPRT group there were still significantly more patients with additional local recurrences and biochemical persistence after prostatectomy. There were no significant differences between both groups in BRFS (p = .97), MFS (p = .43) and NRFS (p = .43). After two years, BRFS, MFS and NRFS were 61%, 86% and 88% in the WPRT group and 57%, 90% and 82% in the HPRT group, respectively. Application of a boost to lymph node metastases, a higher RT dose to the lymphatic pathways (> 50 Gy EQD2α/ß=1.5 Gy) and concomitant androgen deprivation therapy (ADT) were significantly associated with longer BRFS in uni- and multivariate analysis. CONCLUSIONS: Overall, this analysis presents the outcome of HPRT in nodal recurrent prostate cancer patients and shows that it can result in a similar oncologic outcome compared to WPRT. Nevertheless, patients in the WPRT may have been at a higher risk for progression due to some persistent imbalances between the groups. Therefore, further research should prospectively evaluate which subgroups of patients are suitable for HPRT and if HPRT leads to a clinically significant reduction in toxicity.
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PURPOSE: The purpose of this retrospective, multicenter study was to assess efficacy of PSMA-PET/CT-guided salvage radiotherapy (sRT) in patients with recurrent or persistent PSA after primary surgery and PSA levels < 0.2 ng/ml. METHODS: The study included patients from a pooled cohort (n = 1223) of 11 centers from 6 countries. Patients with PSA levels > 0.2 ng/ml prior to sRT or without sRT to the prostatic fossa were excluded. The primary study endpoint was biochemical recurrence-free survival (BRFS) and BR was defined as PSA nadir after sRT + 0.2 ng/ml. Cox regression analysis was performed to assess the impact of clinical parameters on BRFS. Recurrence patterns after sRT were analyzed. RESULTS: The final cohort consisted of 273 patients; 78/273 (28.6%) and 48/273 (17.6%) patients had local or nodal recurrence on PET/CT. The most frequently applied sRT dose to the prostatic fossa was 66-70 Gy (n = 143/273, 52.4%). SRT to pelvic lymphatics was delivered in 87/273 (31.9%) patients and androgen deprivation therapy was given to 36/273 (13.2%) patients. After a median follow-up time of 31.1 months (IQR: 20-44), 60/273 (22%) patients had biochemical recurrence. The 2- and 3-year BRFS was 90.1% and 79.2%, respectively. The presence of seminal vesicle invasion in surgery (p = 0.019) and local recurrences in PET/CT (p = 0.039) had a significant impact on BR in multivariate analysis. In 16 patients, information on recurrence patterns on PSMA-PET/CT after sRT was available and one had recurrent disease inside the RT field. CONCLUSION: This multicenter analysis suggests that implementation of PSMA-PET/CT imaging for sRT guidance might be of benefit for patients with very low PSA levels after surgery due to promising BRFS rates and a low number of relapses within the sRT field.
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Neoplasias da Próstata , Masculino , Humanos , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/radioterapia , Neoplasias da Próstata/cirurgia , Antígeno Prostático Específico , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/métodos , Radioisótopos de Gálio , Estudos Retrospectivos , Antagonistas de Androgênios , Recidiva Local de Neoplasia/diagnóstico por imagem , Recidiva Local de Neoplasia/radioterapia , Terapia de Salvação , ProstatectomiaRESUMO
PURPOSE: To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). MATERIAL AND METHODS: Consecutive patients, who underwent 68Ga-PSMA11-PET/CT-guided sRT from three high-volume centers in Germany, were included in this retrospective multicenter study. Patients had PET-positive local recurrences and were treated with intensity-modulated sRT. Radiomic features were extracted from volumes of interests on CT guided by focal PSMA-PET uptakes. After preprocessing, clinical, radiomics, and combined clinical-radiomic models were developed combining different feature reduction techniques and Cox proportional hazard models within a nested cross validation approach. RESULTS: Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature. CONCLUSION: This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.
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Radioisótopos de Gálio , Neoplasias da Próstata , Masculino , Humanos , Isótopos de Gálio , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/métodos , Prostatectomia , Recidiva Local de Neoplasia/diagnóstico por imagem , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/radioterapia , Neoplasias da Próstata/cirurgiaRESUMO
PURPOSE: This study aims to evaluate the association of the maximum standardized uptake value (SUVmax) in positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET) prior to salvage radiotherapy (sRT) on biochemical recurrence free survival (BRFS) in a large multicenter cohort. METHODS: Patients who underwent 68 Ga-PSMA11-PET prior to sRT were enrolled in four high-volume centers in this retrospective multicenter study. Only patients with PET-positive local recurrence (LR) and/or nodal recurrence (NR) within the pelvis were included. Patients were treated with intensity-modulated-sRT to the prostatic fossa and elective lymphatics in case of nodal disease. Dose escalation was delivered to PET-positive LR and NR. Androgen deprivation therapy was administered at the discretion of the treating physician. LR and NR were manually delineated and SUVmax was extracted for LR and NR. Cox-regression was performed to analyze the impact of clinical parameters and the SUVmax-derived values on BRFS. RESULTS: Two hundred thirty-five patients with a median follow-up (FU) of 24 months were included in the final cohort. Two-year and 4-year BRFS for all patients were 68% and 56%. The presence of LR was associated with favorable BRFS (p = 0.016). Presence of NR was associated with unfavorable BRFS (p = 0.007). While there was a trend for SUVmax values ≥ median (p = 0.071), SUVmax values ≥ 75% quartile in LR were significantly associated with unfavorable BRFS (p = 0.022, HR: 2.1, 95%CI 1.1-4.6). SUVmax value in NR was not significantly associated with BRFS. SUVmax in LR stayed significant in multivariate analysis (p = 0.030). Sensitivity analysis with patients for who had a FU of > 12 months (n = 197) confirmed these results. CONCLUSION: The non-invasive biomarker SUVmax can prognosticate outcome in patients undergoing sRT and recurrence confined to the prostatic fossa in PSMA-PET. Its addition might contribute to improve risk stratification of patients with recurrent PCa and to guide personalized treatment decisions in terms of treatment intensification or de-intensification. This article is part of the Topical Collection on Oncology-Genitourinary.
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Neoplasias da Próstata , Masculino , Humanos , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/radioterapia , Neoplasias da Próstata/cirurgia , Próstata , Antagonistas de Androgênios , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/métodos , Recidiva Local de Neoplasia/diagnóstico por imagem , Tomografia Computadorizada por Raios X , Prostatectomia , Estudos Retrospectivos , Tomografia por Emissão de Pósitrons , Radioisótopos de GálioRESUMO
The transcription factor "nuclear factor erythroid 2" (NFE2) is overexpressed in the majority of patients with myeloproliferative neoplasms (MPNs). In murine models, elevated NFE2 levels cause an MPN phenotype with spontaneous leukemic transformation. However, both the molecular mechanisms leading to NFE2 overexpression and its downstream targets remain incompletely understood. Here, we show that the histone demethylase JMJD1C constitutes a novel NFE2 target gene. JMJD1C levels are significantly elevated in polycythemia vera (PV) and primary myelofibrosis patients; concomitantly, global H3K9me1 and H3K9me2 levels are significantly decreased. JMJD1C binding to the NFE2 promoter is increased in PV patients, decreasing both H3K9me2 levels and binding of the repressive heterochromatin protein-1α (HP1α). Hence, JMJD1C and NFE2 participate in a novel autoregulatory loop. Depleting JMJD1C expression significantly reduced cytokine-independent growth of an MPN cell line. Independently, NFE2 is regulated through the epigenetic JAK2 pathway by phosphorylation of H3Y41. This likewise inhibits HP1α binding. Treatment with decitabine lowered H3Y41ph and augmented H3K9me2 levels at the NFE2 locus in HEL cells, thereby increasing HP1α binding, which normalized NFE2 expression selectively in JAK2V617F-positive cell lines.
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Epigênese Genética , Regulação da Expressão Gênica , Expressão Gênica , Transtornos Mieloproliferativos/genética , Subunidade p45 do Fator de Transcrição NF-E2/genética , Biomarcadores , Homólogo 5 da Proteína Cromobox , Citocinas/metabolismo , Metilação de DNA , Decitabina/farmacologia , Histonas/metabolismo , Humanos , Janus Quinase 2/genética , Janus Quinase 2/metabolismo , Histona Desmetilases com o Domínio Jumonji/genética , Modelos Biológicos , Mutação , Transtornos Mieloproliferativos/metabolismo , Subunidade p45 do Fator de Transcrição NF-E2/metabolismo , Oxirredutases N-Desmetilantes/genética , Fosforilação , Policitemia Vera/genética , Regiões Promotoras Genéticas , Ligação ProteicaRESUMO
BACKGROUND: There are different contouring guidelines for definition of the clinical target volume (CTV) for intensity-modulated radiation therapy (IMRT) of anal cancer (AC). We conducted a planning comparison study to evaluate and compare the dose to relevant organs at risk (OARs) while using different CTV definitions. METHODS: Twelve patients with a primary diagnosis of anal cancer, who were treated with primary chemoradiation (CRT), were selected. We generated four guideline-specific CTVs and subsequently planned target volumes (PTVs) on the planning CT scan of each patient. An IMRT plan for volumetric arc therapy (VMAT) was set up for each PTV. Dose parameters of the planned target volume (PTV) and OARs were evaluated and compared, too. RESULTS: The mean volume of the four PTVs ranged from 2138 cc to 2433 cc. The target volumes contoured by the authors based on the recommendations of each group were similar in the pelvis, while they differed significantly in the inguinal region. There were no significant differences between the four target volumes with regard to the dose parameters of the cranially located OARs. Conversely, some dose parameters concerning the genitals and the skin varied significantly among the different guidelines. CONCLUSION: The four contouring guidelines differ significantly concerning the inguinal region. In order to avoid inguinal recurrence and to protect relevant OARs, further investigations are needed to generate uniform standards for definition of the elective clinical target volume in the inguinal region.
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Neoplasias do Ânus/radioterapia , Órgãos em Risco/efeitos da radiação , Radiometria , Adulto , Idoso , Idoso de 80 Anos ou mais , Neoplasias do Ânus/patologia , Quimiorradioterapia Adjuvante , Estudos de Coortes , Terapia Combinada , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estadiamento de NeoplasiasRESUMO
PURPOSE: In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs). METHODS: Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling. RESULTS: Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node-specific decision curve analysis, there was a clinical net benefit above LN short diameter. CONCLUSION: The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features.
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Neoplasias da Próstata , Cirurgia Assistida por Computador , Humanos , Linfonodos , Metástase Linfática , Masculino , Recidiva Local de Neoplasia , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/cirurgia , Tomografia Computadorizada por Raios XRESUMO
Medical imaging plays an imminent role in today's radiation oncology workflow. Predominantly based on semantic image analysis, malignant tumors are diagnosed, staged, and therapy decisions are made. The field of "radiomics" promises to extract complementary, objective information from medical images. In radiomics, predefined quantitative features including intensity statistics, texture, shape, or filtering techniques are combined into statistical or machine learning models to predict clinical or biological outcomes. Alternatively, deep neural networks can directly analyze medical images and provide predictions. A large number of research studies could demonstrate that radiomics prediction models may provide significant benefits in the radiation oncology workflow including diagnostics, tumor characterization, target volume segmentation, prognostic stratification, and prediction of therapy response or treatment-related toxicities. This chapter provides an overview of techniques within the radiomics toolbox, potential clinical application, and current limitations. A literature overview of four selected malignant entities including non-small cell lung cancer, head and neck squamous cell carcinomas, soft tissue sarcomas, and gliomas is given.
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Neoplasias/diagnóstico por imagem , Neoplasias/radioterapia , Radioterapia (Especialidade) , Humanos , PrognósticoRESUMO
BACKGROUND: There are different contouring guidelines for the clinical target volume (CTV) in anal cancer (AC) which vary concerning recommendations for radiation margins in different anatomical regions, especially on inguinal site. PET imaging has become more important in primary staging of AC as a very sensitive method to detect lymph node (LN) metastases. Using PET imaging, we evaluated patterns of LN spread, and examined the differences of the respective contouring guidelines on the basis of our results. METHODS: We carried out a retrospective study of thirty-seven AC patients treated with chemoradiation (CRT) who underwent FDG-PET imaging for primary staging in our department between 2011 and 2018. Patients showing PET positive LN were included in this analysis. Using a color code, LN metastases of all patients were delineated on a template with "standard anatomy" and were divided indicating whether their location was in- or out-field of the standard CTV as recommended by the Radiation Therapy Oncology Group (RTOG), the Australasian Gastrointestinal Trials Group (AGITG) or the British National Guidance (BNG). Furthermore, a detailed analysis of the location of LN of the inguinal region was performed. RESULTS: Twenty-two out of thirty-seven AC patients with pre-treatment PET imaging had PET positive LN metastases, accumulating to a total of 154 LN. The most commonly affected anatomical region was inguinal (49 LN, 32%). All para-rectal, external/internal iliac, and pre-sacral LN were covered by the recommended CTVs of the three different guidelines. Of forty-nine involved inguinal LN, fourteen (29%), seven (14%) and five (10%) were situated outside of the recommended CTVs by RTOG, AGITG and BNG. Inguinal LN could be located up to 5.7 cm inferiorly to the femoral saphenous junction and 2.8 cm medial or laterally to the big femoral vessels. CONCLUSION: Pelvis-related, various recommendations are largely consistent, and all LN are covered by the recommended CTVs. LN "misses" appear generally cranially (common iliac or para-aortic) or caudally (inguinal) to the recommended CTVs. The established guidelines differ significantly, particular regarding the inguinal region. Based on our results, we presented our suggestions for CTV definition of the inguinal region. LN involvement of a larger number of patients should be investigated to enable final recommendations.
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Neoplasias do Ânus/diagnóstico por imagem , Carcinoma de Células Escamosas/diagnóstico por imagem , Metástase Linfática/diagnóstico por imagem , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada/métodos , Carga Tumoral , Neoplasias do Ânus/tratamento farmacológico , Neoplasias do Ânus/patologia , Carcinoma de Células Escamosas/tratamento farmacológico , Carcinoma de Células Escamosas/patologia , Quimiorradioterapia , Feminino , Humanos , Artéria Ilíaca , Canal Inguinal , Linfonodos/patologia , Masculino , Pessoa de Meia-Idade , Recidiva Local de Neoplasia , Estadiamento de Neoplasias , Pelve , Guias de Prática Clínica como Assunto , Estudos Retrospectivos , Terminologia como AssuntoRESUMO
BACKGROUND: For glioblastoma (GBM), multiple prognostic factors have been identified. Semantic imaging features were shown to be predictive for survival prediction. No similar data have been generated for the prediction of progression. The aim of this study was to assess the predictive value of the semantic visually accessable REMBRANDT [repository for molecular brain neoplasia data] images (VASARI) imaging feature set for progression and survival, and the creation of joint prognostic models in combination with clinical and pathological information. METHODS: 189 patients were retrospectively analyzed. Age, Karnofsky performance status, gender, and MGMT promoter methylation and IDH mutation status were assessed. VASARI features were determined on pre- and postoperative MRIs. Predictive potential was assessed with univariate analyses and Kaplan-Meier survival curves. Following variable selection and resampling, multivariate Cox regression models were created. Predictive performance was tested on patient test sets and compared between groups. The frequency of selection for single variables and variable pairs was determined. RESULTS: For progression free survival (PFS) and overall survival (OS), univariate significant associations were shown for 9 and 10 VASARI features, respectively. Multivariate models yielded concordance indices significantly different from random for the clinical, imaging, combined, and combinedâ¯+ MGMT models of 0.657, 0.636, 0.694, and 0.716 for OS, and 0.602, 0.604, 0.633, and 0.643 for PFS. "Multilocality," "deep white-matter invasion," "satellites," and "ependymal invasion" were over proportionally selected for multivariate model generation, underlining their importance. CONCLUSIONS: We demonstrated a predictive value of several qualitative imaging features for progression and survival. The performance of prognostic models was increased by combining clinical, pathological, and imaging features.
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Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/mortalidade , Progressão da Doença , Glioblastoma/diagnóstico por imagem , Glioblastoma/mortalidade , Interpretação de Imagem Assistida por Computador , Web Semântica , Adulto , Idoso , Idoso de 80 Anos ou mais , Neoplasias Encefálicas/radioterapia , Conjuntos de Dados como Assunto , Intervalo Livre de Doença , Feminino , Glioblastoma/radioterapia , Humanos , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Prognóstico , Estudos Retrospectivos , Software , Adulto JovemRESUMO
BACKGROUND AND PURPOSE: Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these models. MATERIALS AND METHODS: A single-center retrospective cohort of 136 STS patients treated with radiotherapy (RT) was analyzed for patients' characteristics, staging information, and treatment-related data. Therapeutic imaging studies and pathology reports of neoadjuvantly treated patients were analyzed for signs of response. Random forest machine learning-based models were used to predict patients' death and disease progression at 2 years. Pre-treatment and treatment models were compared. RESULTS: The prognostic models achieved high performances. Using treatment features improved the overall performance for all three classification types: prediction of death, and of local and systemic progression (area under the receiver operatoring characteristic curve (AUC) of 0.87, 0.88, and 0.84, respectively). Overall, RT-related features, such as the planning target volume and total dose, had preeminent importance for prognostic performance. Therapy response features were selected for prediction of disease progression. CONCLUSIONS: A machine learning-based prognostic model combining known prognostic factors with treatment- and response-related information showed high accuracy for individualized risk assessment. This model could be used for adjustments of follow-up procedures.
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Aprendizado de Máquina , Modelos de Riscos Proporcionais , Sarcoma/patologia , Sarcoma/radioterapia , Adulto , Idoso , Idoso de 80 Anos ou mais , Estudos de Coortes , Progressão da Doença , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Terapia Neoadjuvante , Estadiamento de Neoplasias , Prognóstico , Estudos Retrospectivos , Medição de Risco , Sarcoma/mortalidade , Taxa de SobrevidaRESUMO
Background: The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. Methods: One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans. Results: The parameter ratio Dw/ρ (Pâ <â .05 in TCGA) as well as the simulated tumor volume (Pâ <â .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans. Conclusions: Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.
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PURPOSE: The identification of internal mammary lymph node metastases and the assessment of associated risk factors are crucial for adjuvant regional lymph node irradiation in patients with breast cancer. The current study aims to investigate whether tumor contact with internal mammary perforator vessels is associated with gross internal mammary lymph node involvement. METHODS AND MATERIALS: We included 297 patients with primary breast cancer and gross internal mammary (IMN+) and/or axillary metastases as well as 230 patients without lymph node metastases. Based on pretreatment dynamic contrast-enhanced magnetic resonance imaging, we assessed contact of the tumor with the internal mammary perforating vessels (IMPV). RESULTS: A total of 59 patients had ipsilateral IMN+ (iIMN+), 10 patients had contralateral IMN+ (cIMN+), and 228 patients had ipsilateral axillary metastases without IMN; 230 patients had node-negative breast cancer. In patients with iIMN+, 100% of tumors had contact with ipsilateral IMPV, with 94.9% (n = 56) classified as major contact. In iIMN- patients, major IMPV contact was observed in only 25.3% (n = 116), and 36.2% (n = 166) had no IMPV contact at all. Receiver operating characteristic analysis revealed that "major IMPV contact" was more accurate in predicting iIMN+ (area under the curve, 0.85) compared with a multivariate model combining grade of differentiation, tumor site, size, and molecular subtype (area under the curve, 0.65). Strikingly, among patients with cIMN+, 100% of tumors had contact with a crossing contralateral IMPV, whereas in cIMN- patients, IMPVs to the contralateral side were observed in only 53.4% (iIMN+) and 24.8% (iIMN-), respectively. CONCLUSIONS: Tumor contact with the IMPV is highly associated with risk of gross IMN involvement. Further studies are warranted to investigate whether this identified risk factor is also associated with microscopic IMN involvement and whether it can assist in the selection of patients with breast cancer for irradiation of the internal mammary lymph nodes.
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Axila , Neoplasias da Mama , Linfonodos , Metástase Linfática , Imageamento por Ressonância Magnética , Humanos , Feminino , Pessoa de Meia-Idade , Neoplasias da Mama/patologia , Neoplasias da Mama/radioterapia , Fatores de Risco , Idoso , Adulto , Linfonodos/patologia , Linfonodos/diagnóstico por imagem , Idoso de 80 Anos ou mais , Artéria Torácica Interna/diagnóstico por imagemRESUMO
Background: Meningiomas are the most common primary brain tumors. While most are benign (WHO grade 1) and have a favorable prognosis, up to one-fourth are classified as higher-grade, falling into WHO grade 2 or 3 categories. Recently, an integrated risk score (IRS) pertaining to tumor biology was developed and its prognostic relevance was validated in a large, multicenter study. We hypothesized imaging data to be reflective of the IRS. Thus, we assessed the potential of a machine learning classifier for its noninvasive prediction using preoperative magnetic resonance imaging (MRI). Methods: In total, 160 WHO grade 2 and 3 meningioma patients from 2 university centers were included in this study. All patients underwent surgery with histopathological workup including methylation analysis. Preoperative MRI scans were automatically segmented, and radiomic parameters were extracted. Using a random forest classifier, 3 machine learning classifiers (1 multiclass classifier for IRS and 2 binary classifiers for low-risk and high-risk prediction, respectively) were developed in a training set (120 patients) and independently tested in a hold-out test set (40 patients). Results: Multiclass IRS classification had a test set area under the curve (AUC) of 0.7, mostly driven by the difficulties in clearly separating medium-risk from high-risk patients. Consequently, a classifier predicting low-risk IRS versus medium-/high-risk showed a very high test accuracy of 90% (AUC 0.88). In particular, "sphericity" was associated with low-risk IRS classification. Conclusion: The IRS, in particular molecular low-risk, can be predicted from imaging data with high accuracy, making this important prognostic classification accessible by imaging.
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BACKGROUND: Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-based automatic segmentation (DLBAS) algorithm to reproducibly predict the primary gross tumor as VOI for Radiomics analyses in extremity soft tissue sarcomas (STS). METHODS: A DLBAS algorithm was trained on a cohort of 157 patients and externally tested on an independent cohort of 87 patients using contrast-enhanced MRI. Manual tumor delineations by a radiation oncologist served as ground truths (GTs). A benchmark study with 20 cases from the test cohort compared the DLBAS predictions against manual VOI segmentations of two residents (ERs) and clinical delineations of two radiation oncologists (ROs). The ROs rated DLBAS predictions regarding their direct applicability. RESULTS: The DLBAS achieved a median dice similarity coefficient (DSC) of 0.88 against the GTs in the entire test cohort (interquartile range (IQR): 0.11) and a median DSC of 0.89 (IQR 0.07) and 0.82 (IQR 0.10) in comparison to ERs and ROs, respectively. Radiomics feature stability was high with a median intraclass correlation coefficient of 0.97, 0.95 and 0.94 for GTs, ERs, and ROs, respectively. DLBAS predictions were deemed clinically suitable by the two ROs in 35% and 20% of cases, respectively. CONCLUSION: The results demonstrate that the DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction. Variability remains regarding direct clinical applicability of predictions for RT treatment planning.
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Algoritmos , Benchmarking , Aprendizado Profundo , Extremidades , Imageamento por Ressonância Magnética , Sarcoma , Humanos , Sarcoma/diagnóstico por imagem , Sarcoma/radioterapia , Sarcoma/patologia , Imageamento por Ressonância Magnética/métodos , Masculino , Feminino , Extremidades/diagnóstico por imagem , Pessoa de Meia-Idade , Adulto , Idoso , Planejamento da Radioterapia Assistida por Computador/métodos , Neoplasias de Tecidos Moles/diagnóstico por imagem , Neoplasias de Tecidos Moles/radioterapia , Neoplasias de Tecidos Moles/patologia , RadiômicaRESUMO
PURPOSE: The European Association of Urology (EAU) proposed a risk stratification (high vs. low risk) for patients with biochemical recurrence (BR) following radical prostatectomy (RP). Here we investigated whether this stratification accurately predicts outcome, particularly in patients staged with PSMA-PET. METHODS: For this study, we used a retrospective database including 1222 PSMA-PET-staged prostate cancer patients who were treated with salvage radiotherapy (SRT) for BR, at 11 centers in 5 countries. Patients with lymph node metastases (pN1 or cN1) or unclear EAU risk group were excluded. The remaining cohort comprised 526 patients, including 132 low-risk and 394 high-risk patients. RESULTS: The median follow-up time after SRT was 31.0 months. The 3-year biochemical progression-free survival (BPFS) was 85.7 % in EAU low-risk versus 69.4 % in high-risk patients (p = 0.002). The 3-year metastasis-free survival (MFS) was 94.4 % in low-risk versus 87.6 % in high-risk patients (p = 0.005). The 3-year overall survival (OS) was 99.0 % in low-risk versus 99.6 % in high-risk patients (p = 0.925). In multivariate analysis, EAU risk group remained a statistically significant predictor of BPFS (p = 0.003, HR 2.022, 95 % CI 1.262-3.239) and MFS (p = 0.013, HR 2.986, 95 % CI 1.262-7.058). CONCLUSION: Our data support the EAU risk group definition. EAU risk grouping for BCR reliably predicted outcome in patients staged lymph node-negative after RP and with PSMA-PET before SRT. To our knowledge, this is the first study validating the EAU risk grouping in patients treated with PSMA-PET-planned SRT.
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Recidiva Local de Neoplasia , Prostatectomia , Neoplasias da Próstata , Terapia de Salvação , Humanos , Masculino , Neoplasias da Próstata/radioterapia , Neoplasias da Próstata/patologia , Neoplasias da Próstata/cirurgia , Terapia de Salvação/métodos , Idoso , Estudos Retrospectivos , Pessoa de Meia-Idade , Medição de Risco , Tomografia por Emissão de Pósitrons , Antígeno Prostático Específico/sangue , Europa (Continente)RESUMO
BACKGROUND: Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk. METHODS: Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of Brain Metastases (AURORA) retrospective study (training cohort: 253 patients from two centers; external test cohort: 99 patients from five centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set. RESULTS: The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (p < 0.001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively. CONCLUSIONS: A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy.
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BACKGROUND: Soft tissue sarcomas (STS) are a relatively rare group of malignant tumors. Currently, there is very little published clinical data, especially in the context of curative multimodal therapy with image-guided, conformal, intensity-modulated radiotherapy. METHODS: Patients who received preoperative or postoperative intensity-modulated radiotherapy for STS of the extremities or trunk with curative intent were included in this single centre retrospective analysis. A Kaplan-Meier analysis was performed to evaluate survival endpoints. Multivariable proportional hazard models were used to investigate the association between survival endpoints and tumour-, patient-, and treatment-specific characteristics. RESULTS: 86 patients were included in the analysis. The most common histological subtypes were undifferentiated pleomorphic high-grade sarcoma (UPS) (27) and liposarcoma (22). More than two third of the patients received preoperative radiation therapy (72%). During the follow-up period, 39 patients (45%) suffered from some type of relapse, mainly remote (31%). The two-years overall survival rate was 88%. The median DFS was 48 months and the median DMFS was 51 months. Female gender (HR 0.460 (0.217; 0.973)) and histology of liposarcomas compared to UPS proved to be significantly more favorable in terms of DFS (HR 0.327 (0.126; 0.852)). CONCLUSION: Conformal, intensity-modulated radiotherapy is an effective treatment modality in the preoperative or postoperative management of STS. Especially for the prevention of distant metastases, the establishment of modern systemic therapies or multimodal therapy approaches is necessary.