AbstractThis 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.

摘要本研究旨在评估圆柱形肿瘤生长率（cTGR）预测放射配体治疗（RLT）后高分化胃肠胰肿瘤早期进展的预测能力，与常规TGR相比。纳入了58名患者，并在基线，RLT和随访期间收集了每位患者的三次CT扫描。

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.

根据RECIST 1.1在随访时评估的RLT反应计算为病变直径随时间的百分比变化（连续值）和四种不同的RECIST类别。使用常规（将病变体积近似为球体）和圆柱形（称为cTGR，将病变体积近似为椭圆形圆柱体）公式计算基线和中期CT之间的TGR。

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).

受试者工作特征（ROC）曲线用于进行性疾病类别预测，表明cTGR优于常规TGR（ROC下面积分别等于1.00和0.92）。多变量分析证实了cTGR在预测连续RLT反应方面的优越性，与传统的（1.45）相比，cTGR的系数更高（1.56）。

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..

这项研究作为概念验证，为未来的临床试验铺平了道路，将cTGR作为评估RLT反应的有价值的工具。。

IntroductionNeuroendocrine neoplasms (NENs) are a group of tumours arising from the diffuse neuroendocrine system. Well-differentiated gastro-entero-pancreatic tumours (GEP-NETs) consist of carcinoids of the gastrointestinal tract and represent the most common NEN subtype. NET reported incidence is 3.56/100.000 cases per year, with an increasing trend in Western countries over the last decades1,2,3,4, principally due to the improved capability of imaging techniques to detect small lesions.

简介神经内分泌肿瘤（NENs）是由弥漫性神经内分泌系统引起的一组肿瘤。分化良好的胃肠胰肿瘤（GEP-NETs）由胃肠道类癌组成，是最常见的NEN亚型。净报告发病率为每年3.56/100.000例，西方国家在过去十年中呈上升趋势1,2,3,4，主要是由于成像技术检测小病变的能力提高。

Based on the World Health Organization (WHO) annual report 2019, GEP-NETs are classified as low-grade (G1), intermediate-grade (G2), and high-grade (G3), according to the proliferative index (MIB1 or Ki-67) that reflects the cellular biological aggressiveness5,6. In line with the Surveillance, Epidemiology and End Results (SEER), at the time of diagnosis, the whole NET population comprises localised disease in 53% cases, locoregional in 20%, and distant metastasis in 27% (from the Surveillance, Epidemiology End Results (SEER) database).

根据世界卫生组织（WHO）2019年年度报告，根据增殖指数（MIB1或Ki-67），GEP-NETs分为低级（G1），中级（G2）和高级（G3）反映细胞生物侵袭性5,6。根据监测，流行病学和最终结果（SEER），在诊断时，整个净人群包括53%的局部疾病，20%的局部区域和27%的远处转移（来自监测，流行病学最终结果（SEER）数据库）。

These last mainly occur in the liver, although mesentery, peritoneum, and abdominal lymph nodes are frequently involved, particularly for small bowel disease7.Surgery is the therapy cornerstone, being the only curative option. Unfortunately, many GEP-NETs are late-diagnosed and therefore are not amenable to curative treatments.

最后一种主要发生在肝脏，尽管肠系膜，腹膜和腹部淋巴结经常受累，特别是对于小肠疾病7。手术是治疗的基石，是唯一的治疗选择。不幸的是，许多GEP-NETs诊断较晚，因此不适合治疗。

In these cases, the systemic approach is mandatory, such as somatostatin analogues (SSA), targeted therapies, radioligand therapy (RLT) or chemotherapies8. RLT is based on a radioligand that targets SSA receptors expressed on well-differentiated GEP-NET cell surfaces. Among radiotracers, 177-Lu-DOTATATE is currently the most widely administered radiodrug, according to EMA and AIFA approval9,10, due to its low toxicity and positi.

在这些情况下，全身方法是强制性的，如生长抑素类似物（SSA），靶向治疗，放射性配体治疗（RLT）或化疗8。RLT基于靶向在分化良好的GEP-NET细胞表面表达的SSA受体的放射性配体。根据EMA和AIFA的批准[9,10]，在放射性示踪剂中，177 Lu-DOTATATE是目前使用最广泛的放射性药物，因为它具有低毒性和阳性。

(1)

(1)

where \(a\) and \(b\) are the major and minor radii of the elliptical base in the axial plane, respectively, and \(h\) is the elliptic cylinder height in the coronal view (example in Fig. 1).The choice to consider this formula was related to the direct observation of the real shape of GEP-NET lesions on CT scan in clinical practice.

其中\（a \）和\（b \）分别是椭圆基底在轴平面上的主半径和次半径，而\（h \）是冠状视图中的椭圆圆柱体高度（图1中的示例）。考虑该公式的选择与临床实践中CT扫描直接观察GEP-NET病变的真实形状有关。

In detail, considering the axial view we noticed that the major and the minor axes are frequently quite diverse so the spherical formula may not include this feature. Furthermore, in the coronal view the height of the lesion could be a further measurement to consider that differs from the previously mentioned axes.

详细地说，考虑到轴向视图，我们注意到长轴和短轴经常非常不同，因此球形公式可能不包括此功能。此外，在冠状视图中，病变的高度可以是进一步的测量，以考虑与前面提到的轴不同。

Indeed, to correctly estimate the lesion volume these three measurements must be borne in mind in the mathematical formula. Hence, we suggest considering a cylinder with an elliptical base as the most appropriate geometric shape for characterizing the lesion. This entails a slightly extended evaluation time for the radiologist, without impacting either the acquisition duration or the patient's participation.

事实上，为了正确估计病变体积，必须在数学公式中牢记这三个测量值。因此，我们建议考虑将具有椭圆形底座的圆柱体作为表征病变的最合适几何形状。这需要放射科医生稍微延长评估时间，而不会影响采集时间或患者的参与。

To numerically confirm the nonsphericity of the identified lesions, we computed the sphericity index, which consists of the ratio of the surface area of a sphere occupying the same volume as the object under examination to the surface area of the object itself37 (Eq. 2).$$\Psi =\frac{\sqrt[3]{36\pi {V}^{2}}}{A}$$.

为了从数值上确认已识别病变的非球形，我们计算了球形指数，该指数由与被检查物体体积相同的球体的表面积与物体本身的表面积之比组成37（等式2）$$\Psi=\frac{\sqrt[3]{36\pi{V}^{2}}}{A}$$。

(2)

(2)

where \(V\) is the volume, estimated using Eq. (1) 37, and \(A\) is the surface area of the lesion, ie., \(A\cong 2\pi ab+2\pi h\sqrt{\frac{{a}^{2}+ {b}^{2}}{2}}\)Statistical analysisWe conducted the same statistical analyses on the whole sample and on a subset of lesions exhibiting the largest discrepancy between conventional and cylindrical TGR values, where \(|TGR-cTGR|> 2\), to examine the effectiveness of introducing cTGR in lesions undergoing morphological changes that affect not only volume but also shape during the longitudinal follow-up.We explored the capabilities of the cTGR to predict early disease progression after only two RLT administrations through two complementary statistical analyses (similarly applied for conventional TGR).

其中\（V \）是体积，使用等式（1）37估算，\（A \）是病变的表面积，即\（A \ cong 2 \ pi ab+2 \ pi h \ sqrt{\ frac{{{A}^{2}+{b}^{2}}{2}}}）统计分析我们对整个样本以及常规和圆柱形TGR值之间差异最大的病变子集进行了相同的统计分析，其中\（| TGR cTGR |>2 \）检查在纵向随访期间，在形态变化不仅影响体积而且影响形状的病变中引入cTGR的有效性。我们通过两个互补的统计分析（类似地适用于常规TGR），探索了cTGR仅在两次RLT给药后预测早期疾病进展的能力。

First, we analysed TGR’s ability to discriminate the class of progression through the Area Under the Receiver Operating Characteristic (AUROC) computation. We compared the AUROC values obtained using either the cTGR or the conventional TGR as predictors of progression class—the higher the AUROC value, the better the model is at distinguishing between different classes of progression.Then, we modelled the relationship between the change in TGR (conventional or cylindrical) and the response to RLT (expressed as the percentage change in the sum of the major diameters of baseline and follow-up lesions), specifying two separate linear regression models, one including conventional TGR and one including cTGR, together with the following covariates: age, gender, primary tumour, ECOG-PS, lines of treatment, WHO grading.

首先，我们分析了TGR在接收器工作特性（AUROC）计算下通过区域区分进展类别的能力。我们比较了使用cTGR或常规TGR作为进展类别预测因子获得的AUROC值。AUROC值越高，模型越能区分不同类别的进展。然后，我们模拟了TGR（常规或圆柱形）的变化与对RLT的反应（表示为基线和随访病变的主要直径之和的百分比变化）之间的关系，指定了两个独立的线性回归模型，一个包括常规TGR，一个包括cTGR，以及以下协变量：年龄，性别，原发性肿瘤，ECOG-PS，治疗线，WHO分级。

Both AIC and BIC38 were computed in order to compare the performance of the two models (dealing with the trade-off between the goodness of fit of the model and the simplicity of the model). The one with the .

计算AIC和BIC38是为了比较两个模型的性能（处理模型拟合优度和模型简单性之间的权衡）。带的那个。

Table 1 Study population (n = 58) clinical and radiological features.Full size tableOur findings support the utility of TGR, particularly in its cylindrical formulation, to predict PD class and changes in SOD. Specifically, in the analysis carried out on the subset of lesions exhibiting the largest discrepancy between conventional and cylindrical TGR values, the Area Under the Receiver Operating Characteristic (ROC) curve (AUROC) for cTGR was 1 (95% CI [1, 1]), while the AUROC for conventional TGR was 0.92 (95% CI [0.8, 1]) (Fig.

表1研究人群（n=58）临床和放射学特征。全尺寸表我们的发现支持TGR的实用性，特别是在其圆柱形配方中，可以预测PD类别和SOD的变化。。

3 and Table 2). The combination of sensitivity and specificity leading to the highest Youden’s index40 corresponded to a cut-off threshold of 9.59 for the cTGR and 5.84 for the TGR (details in Table 2). Applying the cut-offs mentioned above, both cTGR and TGR obtained a sensitivity (i.e., the ability to identify the non-responders correctly) equal to 100%.

3和表2）。导致最高Youden指数40的敏感性和特异性的组合对应于cTGR的临界阈值为9.59，TGR的临界阈值为5.84（详见表2）。。

At the same time, the cTGR achieved a higher specificity (i.e., the ability to identify the responder patients correctly) than the conventional TGR (100% and 88%, respectively)..

同时，cTGR比常规TGR（分别为100%和88%）具有更高的特异性（即正确识别应答患者的能力）。。

Figure 3ROC curves for conventional TGR and cTGR in the subset of lesions exhibiting the largest discrepancy between conventional and cylindrical TGR values. The black dashed line represents the chance level.Full size imageTable 2 Performance of the prediction of the PD class for conventional and cylindrical TGR.Full size tableAdditionally, when predicting the continuous SOD change, the model incorporating cTGR slightly outperformed the model including conventional TGR as one of its predictors.

图3ROC曲线显示常规TGR和cTGR在常规TGR值和圆柱形TGR值之间差异最大的病变子集中。黑色虚线表示机会水平。全尺寸imageTable 2常规和圆柱形TGR的PD类预测性能。全尺寸表此外，在预测连续SOD变化时，包含cTGR的模型略优于包括常规TGR作为其预测因子之一的模型。

This is evident from the lower Akaike Information Criterion (AIC) (214.59 vs. 221.13), lower Bayesian Information Criterion (BIC) (224.96 vs. 231.50), and slightly higher coefficient (1.56 vs. 1.45) associated with cTGR compared to conventional TGR (details in Supplementary Table S1 online).The ability of the TGRs to early discriminate against the PD class was confirmed also in the whole sample.

这从较低的Akaike信息标准（AIC）（214.59 vs.221.13），较低的贝叶斯信息标准（BIC）（224.96 vs.231.50）以及与cTGR相关的略高系数（1.56 vs.1.45）可以明显看出，与传统的TGR相比（详见在线补充表S1）。在整个样本中也证实了TGR早期区分PD类的能力。

Specifically, the AUROC for cTGR was 0.82 (95% CI [0.49, 1]), while the AUROC for conventional TGR was 0.79 (95% CI [0.47, 1]) (Fig. 4 and Table 2). The regression model that incorporated cTGR and the one involving conventional TGR produced highly comparable outcomes in forecasting the continuous change in SOD.

具体而言，cTGR的AUROC为0.82（95%CI[0.49，1]），而常规TGR的AUROC为0.79（95%CI[0.47，1]）（图4和表2）。纳入cTGR和涉及常规TGR的回归模型在预测SOD的连续变化方面产生了高度可比的结果。

These results closely resembled those achieved within the subset of lesions where there was a notable difference between conventional and cylindrical TGR values (details in Supplementary Table S2 online).Figure 4ROC curves for conventional TGR and cTGR in the whole sample. The black dashed line represents the chance level.Full size imageWe graphically reported the relationship between TGR and cTGR in Fig. 5.

这些结果与在常规和圆柱形TGR值之间存在显着差异的病变子集内获得的结果非常相似（详见在线补充表S2）。图4ROC曲线用于整个样本中的常规TGR和cTGR。黑色虚线表示机会水平。全尺寸图像我们在图5中以图形方式报告了TGR和cTGR之间的关系。

As expected, the two measures are linearly and positively correlated, but the TGR values are different depending on the geometrical approximation of the lesion shape (the red d.

正如预期的那样，这两个指标是线性和正相关的，但TGR值是不同的，这取决于病变形状的几何近似（红色d）。

Data availability

数据可用性

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

在当前研究期间生成和/或分析的数据集可根据合理要求从通讯作者处获得。

ReferencesDas, S. & Dasari, A. Epidemiology, incidence, and prevalence of neuroendocrine neoplasms: Are there global differences?. Curr. Oncol. Rep. 23(4), 43. https://doi.org/10.1007/s11912-021-01029-7 (2021).Article

ReferencesDas，S。＆Dasari，A。神经内分泌肿瘤的流行病学，发病率和患病率：是否存在全球差异？。货币。Oncol公司。代表23（4），43。https://doi.org/10.1007/s11912-021-01029-7（2021年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Dasari, A. et al. Trends in the Incidence, prevalence, and survival outcomes in patients with neuroendocrine tumors in the United States. JAMA Oncol. 3(10), 1335. https://doi.org/10.1001/jamaoncol.2017.0589 (2017).Article

Dasari，A。等人。美国神经内分泌肿瘤患者的发病率，患病率和生存结果趋势。JAMA Oncol。。https://doi.org/10.1001/jamaoncol.2017.0589（2017年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Leoncini, E. et al. Increased incidence trend of low-grade and high-grade neuroendocrine neoplasms. Endocrine 58(2), 368–379. https://doi.org/10.1007/s12020-017-1273-x (2017).Article

Leoncini，E.等人，低级和高级神经内分泌肿瘤的发病率呈上升趋势。内分泌58（2），368-379。https://doi.org/10.1007/s12020-017-1273-x（2017年）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

White, B. E. et al. Incidence and survival of neuroendocrine neoplasia in England 1995–2018: A retrospective, population-based study. Lancet Reg. Health–Europe. 23, 100510. https://doi.org/10.1016/j.lanepe.2022.100510 (2022).Article

White，B.E.等人，《1995-2018年英格兰神经内分泌肿瘤的发病率和生存率：一项基于人群的回顾性研究》。柳叶刀注册健康-欧洲。23100510。https://doi.org/10.1016/j.lanepe.2022.100510（2022年）。文章

Google Scholar

谷歌学者

WHO, IARC, eds. Digestive System Tumours. 5th ed. International agency for research on cancer; 2019.Rindi, G. et al. Overview of the 2022 WHO classification of neuroendocrine neoplasms. Endocr. Pathol. 33(1), 115–154. https://doi.org/10.1007/s12022-022-09708-2 (2022).Article

WHO，IARC，eds。消化系统肿瘤。第五版。国际癌症研究机构；2019年。Rindi，G。等人，《2022年世界卫生组织神经内分泌肿瘤分类概述》。内分泌。病理学。33（1），115-154。https://doi.org/10.1007/s12022-022-09708-2（2022年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Cives, M. & Strosberg, J. R. Gastroenteropancreatic neuroendocrine tumors. CA A Cancer J. Clin. 68(6), 471–487. https://doi.org/10.3322/caac.21493 (2018).Article

Cives，M。＆Strosberg，J.R。胃肠胰神经内分泌肿瘤。CA A Cancer J.Clin。68（6），471-487。https://doi.org/10.3322/caac.21493（2018年）。文章

Google Scholar

谷歌学者

Pavel, M. et al. Gastroenteropancreatic neuroendocrine neoplasms: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 31(7), 844–860. https://doi.org/10.1016/j.annonc.2020.03.304 (2020).Article

Pavel，M.等人，《胃肠胰神经内分泌肿瘤：ESMO诊断、治疗和随访临床实践指南》。安科。31（7），844-860。https://doi.org/10.1016/j.annonc.2020.03.304（2020年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

AGENZIA ITALIANA DEL FARMACO | DETERMINA 11 marzo 2019. https://www.aifa.gov.it/documents/20142/847786/Determina_501-2019_Lutathera.pdfLutathera | European Medicines Agency. https://www.ema.europa.eu/en/medicines/human/EPAR/lutatheraStrosberg, J. et al. Phase 3 trial of 177 Lu-dotatate for midgut neuroendocrine tumors.

意大利药品代理| 2019年3月11日确定。https://www.aifa.gov.it/documents/20142/847786/Determina_501-2019_Lutathera.pdfLutathera|欧洲药品管理局。https://www.ema.europa.eu/en/medicines/human/EPAR/lutatheraStrosberg，J.等人177Lu dotatate治疗中肠神经内分泌肿瘤的3期试验。

N. Engl. J. Med. 376(2), 125–135. https://doi.org/10.1056/NEJMoa1607427 (2017).Article .

N.Engl。《医学杂志》376（2），125-135。https://doi.org/10.1056/NEJMoa1607427(2017).文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Strosberg, J. R. et al. 177Lu-Dotatate plus long-acting octreotide versus high-dose long-acting octreotide in patients with midgut neuroendocrine tumours (NETTER-1): Final overall survival and long-term safety results from an open-label, randomised, controlled, phase 3 trial. Lancet Oncol.

Strosberg，J.R.等人177Lu-Dotatate加长效奥曲肽与大剂量长效奥曲肽治疗中肠神经内分泌肿瘤（NETTER-1）患者：最终总生存期和长期安全性结果来自开放标签，随机，对照，3期试验。柳叶刀Oncol。

22(12), 1752–1763. https://doi.org/10.1016/S1470-2045(21)00572-6 (2021).Article .

22(12), 1752-1763.https://doi.org/10.1016/S1470-2045(21)00572-6 (2021).文章联盟。

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 11). Eur. J. Cancer 45(2), 228–247. https://doi.org/10.1016/j.ejca.2008.10.026 (2009).Article

Eisenhauer，E.A.等人，《实体瘤新的反应评估标准：修订的RECIST指南》（第11版）。《欧洲癌症杂志》45（2），228-247。https://doi.org/10.1016/j.ejca.2008.10.026（2009年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Therasse, P. et al. New guidelines to evaluate the response to treatment in solid tumors. JNCI: J. Natl. Cancer Inst. 92(3), 205–216. https://doi.org/10.1093/jnci/92.3.205 (2000).Article

Therasse，P。等人。评估实体瘤治疗反应的新指南。JNCI:J.Natl。癌症研究所92（3），205-216。https://doi.org/10.1093/jnci/92.3.205（2000年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Sundin, A. & Rockall, A. Therapeutic monitoring of gastroenteropancreatic neuroendocrine tumors: The challenges ahead. Neuroendocrinology 96(4), 261–271. https://doi.org/10.1159/000342270 (2012).Article

。神经内分泌学96（4），261-271。https://doi.org/10.1159/000342270（2012年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Van Essen, M., Sundin, A., Krenning, E. P. & Kwekkeboom, D. J. Neuroendocrine tumours: The role of imaging for diagnosis and therapy. Nat. Rev. Endocrinol. 10(2), 102–114. https://doi.org/10.1038/nrendo.2013.246 (2014).Article

Van Essen，M.，Sundin，A.，Krenning，E.P。＆Kwekkeboom，D.J。神经内分泌肿瘤：成像在诊断和治疗中的作用。《国家内分泌杂志》。10（2），102-114。https://doi.org/10.1038/nrendo.2013.246（2014年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Das, S., Al-Toubah, T., El-Haddad, G. & Strosberg, J. 177 Lu-DOTATATE for the treatment of gastroenteropancreatic neuroendocrine tumors. Expert Rev. Gastroenterol. Hepatol. 13(11), 1023–1031. https://doi.org/10.1080/17474124.2019.1685381 (2019).Article

Das，S.，Al Toubah，T.，El Haddad，G。＆Strosberg，J。177 Lu DOTATATE用于治疗胃肠胰神经内分泌肿瘤。胃肠病专家Rev。肝病。13（11），1023-1031。https://doi.org/10.1080/17474124.2019.1685381（2019年）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Hicks, R. J. et al. ENETS consensus guidelines for the standards of care in neuroendocrine neoplasms: Peptide receptor radionuclide therapy with radiolabelled somatostatin analogues. Neuroendocrinology 105(3), 295–309. https://doi.org/10.1159/000475526 (2017).Article

Hicks，R.J.等人，《神经内分泌肿瘤护理标准的ENETS共识指南：用放射性标记的生长抑素类似物进行肽受体放射性核素治疗》。神经内分泌学105（3），295-309。https://doi.org/10.1159/000475526（2017年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Kwekkeboom, D. J. et al. Treatment with the radiolabeled somatostatin analog [177 Lu-DOTA 0, Tyr 3 ] octreotate: Toxicity, efficacy, and survival. JCO 26(13), 2124–2130. https://doi.org/10.1200/JCO.2007.15.2553 (2008).Article

Kwekkeboom，D.J.等人。用放射性标记的生长抑素类似物[177 Lu-DOTA 0，Tyr 3]奥曲酸盐治疗：毒性，疗效和存活率。JCO 26（13），2124-2130。https://doi.org/10.1200/JCO.2007.15.2553（2008年）。文章

CAS

中科院

Google Scholar

谷歌学者

Choi, U. S., Kawaguchi, H., Matsuoka, Y., Kober, T. & Kida, I. Brain tissue segmentation based on MP2RAGE multi-contrast images in 7 T MRI. PLoS One https://doi.org/10.1371/journal.pone.0210803 (2019).Article

Choi，美国，Kawaguchi，H.，Matsuoka，Y.，Kober，T。＆Kida，I。基于MP2RAGE多对比度图像的7 T MRI脑组织分割。PLoS Onehttps://doi.org/10.1371/journal.pone.0210803（2019年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Huizing, D. M. V. et al. Early response assessment and prediction of overall survival after peptide receptor radionuclide therapy. Cancer Imaging 20(1), 57. https://doi.org/10.1186/s40644-020-00335-w (2020).Article

Huizing，D.M.V.等人。肽受体放射性核素治疗后的早期反应评估和总生存率预测。癌症成像20（1），57。https://doi.org/10.1186/s40644-020-00335-w（2020年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Luo, Y. et al. Early evaluation of sunitinib for the treatment of advanced gastroenteropancreatic neuroendocrine neoplasms via CT imaging: RECIST 1.1 or Choi Criteria?. BMC Cancer 17(1), 154. https://doi.org/10.1186/s12885-017-3150-7 (2017).Article

Luo，Y.等。通过CT成像对舒尼替尼治疗晚期胃肠胰神经内分泌肿瘤的早期评估：RECIST 1.1或Choi标准？。BMC癌症17（1），154。https://doi.org/10.1186/s12885-017-3150-7（2017年）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Solis-Hernandez, M. P. et al. Evaluating radiological response in pancreatic neuroendocrine tumours treated with sunitinib: comparison of Choi versus RECIST criteria (CRIPNET_ GETNE1504 study). Br. J. Cancer 121(7), 537–544. https://doi.org/10.1038/s41416-019-0558-7 (2019).Article

Solis Hernandez，M.P.等人，《评估舒尼替尼治疗的胰腺神经内分泌肿瘤的放射学反应：Choi与RECIST标准的比较》（CRIPNET\GETNE1504研究）。Br.J.癌症121（7），537-544。https://doi.org/10.1038/s41416-019-0558-7（2019年）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Van Treijen, M. J. C. et al. Defining disease status in gastroenteropancreatic neuroendocrine tumors: Choi-criteria or RECIST?. Abdom. Radiol. 47(3), 1071–1081. https://doi.org/10.1007/s00261-021-03393-1 (2022).Article

Van Treijen，M.J.C.等人，《定义胃肠胰神经内分泌肿瘤的疾病状态：Choi标准或RECIST？》？。阿布多姆。放射性。47（3），1071-1081。https://doi.org/10.1007/s00261-021-03393-1（2022年）。文章

Google Scholar

谷歌学者

Rodallec, M. et al. Endocrine pancreatic tumours and helical CT: Contrast enhancement is correlated with microvascular density, histoprognostic factors and survival. Pancreatology 6(1–2), 77–85. https://doi.org/10.1159/000090026 (2006).Article

内分泌胰腺肿瘤和螺旋CT：对比度增强与微血管密度、组织预后因素和生存率相关。胰腺学6（1-2），77-85。https://doi.org/10.1159/000090026（2006年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Ferté, C. et al. Tumor growth rate is an early indicator of antitumor drug activity in phase I clinical trials. Clin. Cancer Res. 20(1), 246–252. https://doi.org/10.1158/1078-0432.CCR-13-2098 (2014).Article

。临床。癌症研究20（1），246-252。https://doi.org/10.1158/1078-0432.CCR-13-2098（2014年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Smith, A. D., Shah, S. N., Rini, B. I., Lieber, M. L. & Remer, E. M. Morphology, attenuation, size, and structure (MASS) criteria: Assessing response and predicting clinical outcome in metastatic renal cell carcinoma on antiangiogenic targeted therapy. Am. J. Roentgenol. 194(6), 1470–1478.

Smith，A.D.，Shah，S.N.，Rini，B.I.，Lieber，M.L.＆Remer，E.M。形态，衰减，大小和结构（质量）标准：评估抗血管生成靶向治疗转移性肾细胞癌的反应和预测临床结果。美国J·伦琴。194（6），1470-1478年。

https://doi.org/10.2214/AJR.09.3456 (2010).Article .

https://doi.org/10.2214/AJR.09.3456(2010).文章

Google Scholar

谷歌学者

Stein, W. D. et al. Analyzing the pivotal trial that compared sunitinib and IFN-α in renal cell carcinoma, using a method that assesses tumor regression and growth. Clin. Cancer Res. 18(8), 2374–2381. https://doi.org/10.1158/1078-0432.CCR-11-2275 (2012).Article

Stein，W.D.等人使用评估肿瘤消退和生长的方法，分析了比较舒尼替尼和IFN-α在肾细胞癌中的关键试验。临床。癌症研究18（8），2374-2381。https://doi.org/10.1158/1078-0432.CCR-11-2275（2012年）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

De Mestier, L. et al. Evaluating digestive neuroendocrine tumor progression and therapeutic responses in the era of targeted therapies: State of the art. Endocr.-Relat. Cancer 21(3), R105–R120. https://doi.org/10.1530/ERC-13-0365 (2014).Article

-相关。癌症21（3），R105-R120。https://doi.org/10.1530/ERC-13-0365（2014年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Dromain, C. et al. Tumor growth rate to predict the outcome of patients with neuroendocrine tumors: Performance and sources of variability. Neuroendocrinology 111(9), 831–839. https://doi.org/10.1159/000510445 (2021).Article

Dromain，C.等人。预测神经内分泌肿瘤患者预后的肿瘤生长率：表现和变异来源。神经内分泌学111（9），831-839。https://doi.org/10.1159/000510445（2021年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Lamarca, A. et al. Value of tumor growth rate (TGR) as an early biomarker predictor of patients’ outcome in neuroendocrine tumors (NET)—the GREPONET study. Oncologist. 24(11), e1082–e1090. https://doi.org/10.1634/theoncologist.2018-0672 (2019).Article

Lamarca，A。等人。肿瘤生长率（TGR）作为神经内分泌肿瘤（NET）患者预后的早期生物标志物预测因子的价值-GREPONET研究。肿瘤学家。24（11），e1082–e1090。https://doi.org/10.1634/theoncologist.2018-0672（2019年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Lamarca, A. et al. Tumor growth rate as a validated early radiological biomarker able to reflect treatment-induced changes in neuroendocrine tumors: The GREPONET-2 study. Clin. Cancer Res. 25(22), 6692–6699. https://doi.org/10.1158/1078-0432.CCR-19-0963 (2019).Article

Lamarca，A。等人。肿瘤生长率作为一种经过验证的早期放射生物标志物，能够反映神经内分泌肿瘤治疗引起的变化：GREPONET-2研究。临床。癌症研究25（22），6692-6699。https://doi.org/10.1158/1078-0432.CCR-19-0963（2019年）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Dromain, C. et al. Tumor growth rate as a metric of progression, response, and prognosis in pancreatic and intestinal neuroendocrine tumors. BMC Cancer 19(1), 66. https://doi.org/10.1186/s12885-018-5257-x (2019).Article

Dromain，C.等人。肿瘤生长率作为胰腺和肠道神经内分泌肿瘤进展，反应和预后的指标。BMC癌症19（1），66。https://doi.org/10.1186/s12885-018-5257-x（2019年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Pettersson, O. J., Fröss-Baron, K., Crona, J. & Sundin, A. Tumor growth rate in pancreatic neuroendocrine tumor patients undergoing PRRT with 177Lu-DOTATATE. Endocr. Connect. 10(4), 422–431. https://doi.org/10.1530/EC-21-0027 (2021).Article

Pettersson，O.J.，Fröss Baron，K.，Crona，J。＆Sundin，A。接受177Lu-DOTATATE PRRT的胰腺神经内分泌肿瘤患者的肿瘤生长率。内分泌。连接。10（4），422-431。https://doi.org/10.1530/EC-21-0027（2021年）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Wang, S. J. et al. Baseline tumor growth rate highlights the heterogeneity of well differentiated gastroenteropancreatic neuroendocrine tumors and predicts for increases in Ki67 index over time. J. Neuroendocrinol. 35(4), e13260. https://doi.org/10.1111/jne.13260 (2023).Article

Wang，S.J.等人。基线肿瘤生长率突出了分化良好的胃肠胰神经内分泌肿瘤的异质性，并预测随着时间的推移Ki67指数会增加。J、 神经内分泌。35（4），e13260。https://doi.org/10.1111/jne.13260（2023年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Collins, V. P. Observations on growth rates of human tumors. AJR. 76, 988–1000 (1956).CAS

。AJR公司。76988-1000（1956）。中科院

Google Scholar

谷歌学者

Wadell, H. Volume, shape, and roundness of rock particles. J. Geol. 40(5), 443–451. https://doi.org/10.1086/623964 (1932).Article

Wadell，H。岩石颗粒的体积，形状和圆度。J、 杰尔。40（5），443-451。https://doi.org/10.1086/623964（1932年）。文章

ADS

广告

Google Scholar

谷歌学者

Akaike, H. Information theory and an extension of the maximum likelihood principle. In: Selected Papers of Hirotugu Akaike. Springer; 1998:199–213.Schwarz, G. Estimating the dimension of a model. Ann. Statist. https://doi.org/10.1214/aos/1176344136 (1978).Article

Akaike，H。信息论和最大似然原理的扩展。在：赤池弘图的论文选集。斯普林格；1998:199–213.Schwarz，G。估计模型的维度。安。统计学家。https://doi.org/10.1214/aos/1176344136（1978年）。文章

MathSciNet

MathSciNet

Google Scholar

谷歌学者

Youden, W. J. Index for rating diagnostic tests. Cancer 3(1), 32–35. https://doi.org/10.1002/1097-0142(1950)3:1%3c32::AID-CNCR2820030106%3e3.0.CO;2-3 (1950).Article

Youden，W.J。评估诊断测试的索引。癌症3（1），32-35。https://doi.org/10.1002/1097-0142（1950）3:1%3c32：：AID-CNCR2820030106%3e3.0.CO；2-3（1950年）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Prasad, V. et al. Lessons from a multicentre retrospective study of peptide receptor radionuclide therapy combined with lanreotide for neuroendocrine tumours: A need for standardised practice. Eur. J. Nucl. Med. Mol. Imaging 47(10), 2358–2371. https://doi.org/10.1007/s00259-020-04712-2 (2020).Article .

。欧洲核子公司。医学分子成像47（10），2358-2371。https://doi.org/10.1007/s00259-020-04712-2（2020年）。文章。

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Caplin, M. E. et al. Lanreotide in metastatic enteropancreatic neuroendocrine tumors. N. Eng. J. Med. 371(3), 224–233 (2014).Article

Caplin，M.E.等人，兰瑞肽治疗转移性肠胰神经内分泌肿瘤。N、 《英格兰医学杂志》371（3），224-233（2014）。文章

Google Scholar

谷歌学者

Download referencesAuthor informationAuthor notesThese authors contributed equally: Federica Scalorbi and Enrico Matteo Garanzini.Authors and AffiliationsNuclear Medicine Department, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, ItalyFederica Scalorbi, Giovanni Argiroffi & Marco MaccauroDepartment of Radiodiagnostics and Radiotherapy, IRCCS Fondazione Istituto Nazionale Tumori, Milan, ItalyEnrico Matteo Garanzini, Giuseppina Calareso, Gabriella Di Rocco & Alfonso MarchianòDepartment of Statistics, Computer Science, Applications “G.

下载参考资料作者信息作者注释两位作者贡献相同：费代丽卡·斯卡洛比和恩里科·马特奥·加兰齐尼。作者和附属机构核医学部，IRCCS国家肿瘤研究所，米兰，意大利Federica Scalorbi，Giovanni Argiroffi和Marco Maccauro放射诊断和放射治疗部，IRCCCS国家肿瘤研究基金会，米兰，意大利Enrico Matteo Garanzini，Giuseppina Calareso，Gabriella Di Rocco和Alfonso Marchianå统计、计算机科学和应用部“G。

Parenti”, University of Florence, Viale Morgagni 59, 50134, Florence, ItalyChiara Marzi & Michela BacciniPost-Graduation School of Radiology, Department of Health Sciences, University of Milan, Milan, ItalyGabriella Di RoccoDepartment of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, ItalySara PuscedduAuthorsFederica ScalorbiView author publicationsYou can also search for this author in.

Parenti”，佛罗伦萨大学，Viale Morgagni 5950134，佛罗伦萨，ItalyChiara Marzi＆Michela BacciniPost放射学研究生院，米兰大学健康科学系，米兰，ItalyGabriella Di RoccoDepartment of Medical Oncology，Fondazione IRCCS Istituto Nazionale dei Tumori，Milan，ItalySara PuscedduAuthorsFederica ScalorbiView author Publications你也可以在中搜索这位作者。

PubMed Google ScholarEnrico Matteo GaranziniView author publicationsYou can also search for this author in

PubMed Google ScholarEnrico Matteo GaranziniView作者出版物您也可以在

PubMed Google ScholarGiuseppina CalaresoView author publicationsYou can also search for this author in

PubMed Google ScholarGiuseppina CalaresoView作者出版物您也可以在

PubMed Google ScholarChiara MarziView author publicationsYou can also search for this author in

PubMed Google ScholarChiara MarziView作者出版物您也可以在

PubMed Google ScholarGabriella Di RoccoView author publicationsYou can also search for this author in

PubMed Google ScholarGabriella Di RoccoView作者出版物您也可以在

PubMed Google ScholarGiovanni ArgiroffiView author publicationsYou can also search for this author in

PubMed Google Scholargiovani Argiroffiew作者出版物您也可以在

PubMed Google ScholarMichela BacciniView author publicationsYou can also search for this author in

PubMed Google ScholarMichela BacciniView作者出版物您也可以在

PubMed Google ScholarSara PuscedduView author publicationsYou can also search for this author in

PubMed Google ScholarSara PuscedduView作者出版物您也可以在

PubMed Google ScholarAlfonso MarchianòView author publicationsYou can also search for this author in

PubMed Google ScholarAlfonso MarchianòView作者出版物您也可以在

PubMed Google ScholarMarco MaccauroView author publicationsYou can also search for this author in

PubMed Google ScholarMarco MaccauroView作者出版物您也可以在

PubMed Google ScholarContributionsConception and design of the work: F.S., C.M., M.B., E.M.G.; Data collection: F.S., G.C., E.M.G., G.D.R., G.A.; Data analysis and interpretation: M.B., C.M., F.S.; Drafting the article: F.S., C.M., E.M.G., G.C., M.M., G.D.R., G.A.; Critical revision of the article: M.M., A.M., S.P., M.B.; All authors approve the final version to be published.Corresponding authorCorrespondence to.

PubMed谷歌学术贡献作品的概念和设计：F.S.，C.M.，M.B.，E.M.G。；数据收集：F.S.，G.C.，E.M.G.，G.D.R.，G.A。；数据分析和解释：M.B.，C.M.，F.S。；。；文章的批判性修订：M.M.，A.M.，S.P.，M.B。；所有作者都批准要发布的最终版本。对应作者对应。

Chiara Marzi.Ethics declarations

基亚拉·马齐。道德宣言

Competing interests

相互竞争的利益

Dr. Federica Scalorbi, Dr. Marco Maccauro, and Dr. Sara Pusceddu received honoraria as consultants and speakers from AAA, outside the submitted work. The remaining authors declare no competing interests.

Federica Scalorbi博士、Marco Maccauro博士和Sara Pusceddu博士在提交的作品之外获得了AAA的顾问和演讲者荣誉。其余作者声明没有利益冲突。

Additional informationPublisher's noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationSupplementary Tables.Rights and permissions

Additional informationPublisher的noteSpringer Nature在已发布地图和机构隶属关系中的管辖权主张方面保持中立。补充信息补充表。权限和权限

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

开放获取本文是根据知识共享署名4.0国际许可证授权的，该许可证允许以任何媒体或格式使用，共享，改编，分发和复制，只要您对原始作者和来源给予适当的信任，提供知识共享许可证的链接，并指出是否进行了更改。

The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

本文中的图像或其他第三方材料包含在文章的知识共享许可中，除非在材料的信用额度中另有说明。如果材料未包含在文章的知识共享许可证中，并且您的预期用途未被法律法规允许或超出允许的用途，则您需要直接获得版权所有者的许可。

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/..

要查看此许可证的副本，请访问http://creativecommons.org/licenses/by/4.0/..

Reprints and permissionsAbout this articleCite this articleScalorbi, F., Garanzini, E.M., Calareso, G. et al. Cylindrical TGR as early radiological predictor of RLT progression in GEPNETs: a proof of concept.

转载和许可本文引用本文Scalorbi，F.，Garanzini，E.M.，Calareso，G。等人。圆柱形TGR作为GEPNET中RLT进展的早期放射学预测因子：概念证明。

Sci Rep 14, 15782 (2024). https://doi.org/10.1038/s41598-024-66668-9Download citationReceived: 04 January 2024Accepted: 03 July 2024Published: 09 July 2024DOI: https://doi.org/10.1038/s41598-024-66668-9Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Sci Rep 1415782（2024）。https://doi.org/10.1038/s41598-024-66668-9Download引文接收日期：2024年1月4日接受日期：2024年7月3日发布日期：2024年7月9日OI：https://doi.org/10.1038/s41598-024-66668-9Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

Provided by the Springer Nature SharedIt content-sharing initiative

由Springer Nature SharedIt内容共享计划提供

KeywordsGEP-NETsTGRRLTRECISTv1.1Disease progression

关键词GEP-NETsTGRRLTRECISTv1.1疾病进展

CommentsBy submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

评论通过提交评论，您同意遵守我们的条款和社区指南。如果您发现有虐待行为或不符合我们的条款或准则，请将其标记为不合适。