ChiCTR2600126504 版本V1.0 版本创建时间2026/06/10 09:54:49 中国临床试验注册中心

审核状态：

Project audit state：

通过审核

Successful

注册号：

Registration number：

 ChiCTR2600126504 

最近更新日期：

Date of Last Refreshed on：

 2026-06-10 09:54:27 

注册时间：

Date of Registration：

 2026-06-10 00:00:00 

注册号状态：

补注册

Registration Status：

Retrospective registration

注册题目：

深度学习与先进光学成像技术的巧妙融合：全尺度三维空间转录组学

Public title：

The Ingenious Integration of Deep Learning and Advanced Optical Imaging Technologies: Full-Scale 3D Spatial Transcriptomics

注册题目简写：

English Acronym：

研究课题的正式科学名称：

深度学习与先进光学成像技术的巧妙融合：全尺度三维空间转录组学

Scientific title：

The Ingenious Integration of Deep Learning and Advanced Optical Imaging Technologies: Full-Scale 3D Spatial Transcriptomics

研究课题代号(代码)：

Study subject ID：

在二级注册机构或其它机构的注册号：

The registration number of the Partner Registry or other register：

申请注册联系人：

俞甲子 

研究负责人：

俞甲子 

Applicant：

Jiazi Yu 

Study leader：

Jiazi Yu 

申请注册联系人电话：

Applicant telephone：

 +86 13456138978

研究负责人电话：

Study leader's
telephone：

+86 13456138978

申请注册联系人传真 ：

Applicant Fax：

研究负责人传真：

Study leader's fax：

申请注册联系人电子邮件：

Applicant E-mail：

 lhlyujiazi@nbu.edu.cn

研究负责人电子邮件：

Study leader's E-mail：

 jiazi777@yeah.net

申请单位网址(自愿提供)：

Applicant website(voluntary supply)：

研究负责人网址(自愿提供)：

Study leader's website(voluntary supply)：

申请注册联系人通讯地址：

浙江省宁波市鄞州区江南路111号

研究负责人通讯地址：

浙江省宁波市鄞州区江南路111号

Applicant address：

111 Jiangnan Road, Yinzhou District, Ningbo City, Zhejiang Province

Study leader's address：

111 Jiangnan Road, Yinzhou District, Ningbo City, Zhejiang Province

申请注册联系人邮政编码：

Applicant postcode：

研究负责人邮政编码：

Study leader's postcode：

申请人所在单位：

宁波市医疗中心李惠利医院

Applicant's institution：

Ningbo Medical Centre Lihuili Hospital

研究负责人所在单位：

宁波市医疗中心李惠利医院

Affiliation of the Leader：

Ningbo Medical Centre Lihuili Hospital

是否获伦理委员会批准：

是

Approved by ethic committee：

Yes

伦理委员会批件文号：

Approved No. of ethic committee：

 李惠利医院伦审2026研第170号

伦理委员会批件附件：

Approved file of Ethical Committee：

 查看附件View

批准本研究的伦理委员会名称：

宁波市医疗中心李惠利医院医学伦理委员会

Name of the ethic committee：

Medical Ethics Committee of Ningbo Medical Center Li Huili Hospital

伦理委员会批准日期：

Date of approved by ethic committee：

 2026-05-09 00:00:00

伦理委员会联系人：

章培

Contact Name of the ethic committee：

Zhang Pei

伦理委员会联系地址：

浙江省宁波市鄞州区江南路111号

Contact Address of the ethic committee：

111 Jiangnan Road, Yinzhou District, Ningbo City, Zhejiang Province

伦理委员会联系人电话：

Contact phone of the ethic committee：

 +86 574 87018834

伦理委员会联系人邮箱：

Contact email of the ethic committee：

 542805676@qq.com

研究实施负责（组长）单位：

宁波市医疗中心李惠利医院

Primary sponsor：

Ningbo Medical Centre Lihuili Hospital

研究实施负责（组长）单位地址：

浙江省宁波市鄞州区江南路111号

Primary sponsor's address：

111 Jiangnan Road, Yinzhou District, Ningbo City, Zhejiang Province

试验主办单位(项目批准或申办者)：

Secondary sponsor：

国家：

中国

省(直辖市)：

浙江省

市(区县)：

Country：

China

Province：

Zhejiang

City：

单位(医院)：

宁波市医疗中心李惠利医院

具体地址：

浙江省宁波市鄞州区江南路111号

Institution
hospital：

Ningbo Medical Centre Lihuili Hospital

Address：

111 Jiangnan Road, Yinzhou District, Ningbo City, Zhejiang Province

经费或物资来源：

自筹

Source(s) of funding：

self-financed

研究疾病：

结直肠恶性肿瘤  

Target disease：

Colorectal malignant tumor

研究疾病代码：

Target disease code：

研究类型：

观察性研究

Study type：

Observational study

研究所处阶段：

 其它 

Study phase：

N/A

研究设计：

横断面 

Study design：

Cross-sectional 

研究目的：

本研究提出将深度学习算法与双光子荧光成像技术相结合的创新方案，旨在突破传统空间转录组学的局限，实现三维空间转录信息的获取。具体而言，我们充分发挥深度学习的独特优势，对双光子荧光成像技术生成的复杂数据进行分析与解读。通过在空间转录组学和成像数据的大型数据集上训练深度学习模型，我们能够提取出其他方法难以辨识的有意义特征和模式。这使得我们能够以高精度重建组织内基因表达的三维空间分布。除了实现三维空间转录信息采集外，本研究还通过建立一种在细胞、组织和活体水平获取全尺度空间转录信息的方法，为该领域做出了创新性贡献。这一突破得益于跨尺度光学信息的一致性，使我们能够弥合不同生物组织层次间的知识鸿沟。通过整合多尺度数据，我们得以更全面地理解基因表达的空间调控机制及其对疾病发展和治疗的潜在影响。总体而言，本研究填补了空间转录组学领域的重要技术空白，为探究疾病成因和治疗策略提供了新型快速诊断方法。该方法有望显著提升我们对各类疾病分子机制的理解，并推动个性化医疗的发展。  

Objectives of Study：

This study proposes an innovative approach that combines deep learning algorithms with two-photon fluorescence imaging technology, aiming to overcome the limitations of traditional spatial transcriptomics and achieve the acquisition of three-dimensional spatial transcript information. Specifically, we fully leverage the unique advantages of deep learning to analyze and interpret the complex data generated by two-photon fluorescence imaging technology. By training deep learning models on large datasets of spatial transcriptomics and imaging data, we are able to extract meaningful features and patterns that are difficult to identify by other methods. This enables us to reconstruct the three-dimensional spatial distribution of gene expression within tissues with high precision. In addition to achieving the acquisition of three-dimensional spatial transcript information, this study also makes an innovative contribution to the field by establishing a method for obtaining full-scale spatial transcript information at the cellular, tissue, and in vivo levels. This breakthrough is facilitated by the consistency of cross-scale optical information, allowing us to bridge the knowledge gap between different levels of biological tissues. By integrating multi-scale data, we are able to gain a more comprehensive understanding of the spatial regulatory mechanisms of gene expression and their potential impact on disease progression and treatment. Overall, this study fills an important technological gap in the field of spatial transcriptomics and provides a novel rapid diagnostic method for investigating disease causes and treatment strategies. This method is expected to significantly enhance our understanding of the molecular mechanisms of various diseases and promote the development of personalized medicine.

药物成份或治疗方案详述：

 

Description for medicine or protocol of treatment in detail：

 

纳入标准：

1.诊断与来源：经病理证实为结直肠癌，拟行根治性或姑息性手术切除；
2.样本可获得性与质量要求：能够在规定时间窗口内完成组织处理；组织量满足双光子成像与空间转录组检测的最低需求。
3.临床资料完整性：可获取核心临床病理信息：年龄、性别、肿瘤部位（结肠/直肠、左右半结肠）、TNM分期、分化程度、是否存在淋巴/血管/神经侵犯、治疗信息（是否新辅助治疗等）。
4.知情同意：受试者本人或法定代理人签署组织样本采集与科研使用的书面知情同意书（含基因检测/转录组检测相关条款，若适用）。

Inclusion criteria

1.Diagnosis and source: Confirmed by pathology as colorectal cancer, planned for radical or palliative surgical resection;
2.Sample availability and quality requirements: able to complete organizational processing within the specified time window; The organizational quantity meets the minimum requirements for two-photon imaging and spatial transcriptome detection.
3.Integrity of clinical data: Core clinical and pathological information can be obtained, including age, gender, tumor location (colon/rectum, left and right colon), TNM staging, differentiation degree, presence of lymphatic/vascular/nerve invasion, and treatment information (whether neoadjuvant therapy is used, etc.).
4.Informed consent: The subject or their legal representative signs a written informed consent form for the collection of tissue samples and scientific research use (including relevant clauses for genetic testing/transcriptome testing, if applicable).

排除标准：

1.病理类型不符合主要队列：术后病理提示为非腺癌为主的类型（如神经内分泌肿瘤、淋巴瘤、GIST、鳞癌等）且研究方案未预设纳入者。 2.治疗因素导致转录组/组织结构显著改变且不适合主要分析:术前接受新辅助放化疗、免疫治疗或靶向治疗，导致组织坏死比例高、肿瘤残存极少或RNA质量明显受损者。 3.样本质量不达标 (1)组织离体后处理超出SOP规定时间，或固定/冷冻/保存条件不符合要求； (2)组织严重自溶/机械挤压/电凝灼伤，无法满足双光子成像或空间转录组检测； (3)RNA质量或测序/建库质控不合格（如RIN/ DV200/空间转录平台质控指标未达标——你可用“按平台质控标准判定”表述，避免写死数值）； (4)成像数据严重伪影、信噪比不足或无法完成跨模态配准（如切片折叠、脱片、强背景荧光等），且无法重采。 4.样本代表性不足 ： (1)仅能获得极少量组织，无法同时满足（或至少满足其一）双光子成像与空间转录组检测最低需求； (2)肿瘤组织中肿瘤细胞含量过低（例如几乎全为坏死/炎症渗出），经病理评估不具备分析意义。 5.合并其他肿瘤或特殊情形影响解释 ： (1)合并其他活动性恶性肿瘤（除非为治愈后稳定期且方案允许）； (2)明确遗传综合征相关肿瘤（如Lynch、FAP）若你不计划纳入（可写“未预设分层者排除”；若计划纳入，应改为记录并分层）。

Exclusion criteria：

1. Pathological type does not match the main cohort: Postoperative pathology indicates predominantly non-adenocarcinoma types (such as neuroendocrine tumors, lymphoma, GIST, squamous cell carcinoma, etc.) and the study protocol is not pre-included. 2. Therapeutic factors causing significant changes in transcriptome/tissue structure that are unsuitable for primary analysis: preoperative neoadjuvant chemoradiotherapy, immunotherapy, or targeted therapy resulting in high tissue necrosis rates, minimal tumor remnants, or marked RNA quality impairment. 3. Sample quality does not meet standards (1) Post-ex vivo tissue processing exceeds the SOP time, or fixation/freezing/preservation conditions do not meet requirements; (2) Severe tissue autolysis, mechanical compression, electrocautery burns, unable to meet two-photon imaging or spatial transcriptome detection requirements; (3) RNA quality or sequencing/library quality control is substandard (e.g., RIN/DV200/spatial transcription platform quality control indicators not met—you can use the phrase "determined according to platform quality control standards" to avoid writing down the values); (4) Imaging data are severely artificial, have insufficient signal-to-noise ratio, or cannot achieve cross-modal registration (such as slicing folding, detachment, strong background fluorescence, etc.), and cannot be re-sampled. 4. Insufficient sample representativeness: (1) Only a very small amount of tissue can be obtained, and cannot simultaneously meet (or at least meet one) minimum requirements for two-photon imaging and spatial transcriptome detection; (2) The tumor tissue contains too few tumor cells (for example, almost all are necrotic or inflammatory exudation), and pathological assessment does not provide analytical significance. 5. Explanation of the impact of other tumors or special circumstances: (1) Combined with other active malignant tumors (unless in a stable period after cure and allowed by the regimen); (2) Clearly identify tumors related to genetic syndromes (such as Lynch, FAP). If you do not plan to include (you can write "excluding those without preset stratification"); If included in the plan, it should be changed to recording and layered).

研究实施时间：

Study execute time：

从 From 2025-06-01 00:00:00至 To 2026-12-30 00:00:00  

征募观察对象时间：

Recruiting time：

从 From 2026-06-10 00:00:00 至 To 2026-10-01 00:00:00

干预措施：

Interventions：

组别：

观察组

样本量：

 61

Group：

Observation group

Sample size：

干预措施：

无

干预措施代码：

Intervention：

none

Intervention code：

研究实施地点：

Countries of recruitment and research settings：

国家：

 中国

省(直辖市)：

 浙江省 

市(区县)：

  

Country：

China

Province：

Zhejiang

City：

单位(医院)：

 宁波市医疗中心李惠利医院 

单位级别：

 三级甲等 

Institution
hospital：

Ningbo Medical Centre Lihuili Hospital

Level of the institution：

Tertiary A

测量指标：

Outcomes：

指标中文名：

基因表达空间预测准确性

指标类型：

主要指标

Outcome：

Accuracy of predicting gene expression space

Type：

Primary indicator

测量时间点：

肿瘤样本处理后

测量方法：

Measure time point of outcome：

After tumor sample processing

Measure method：

采集人体标本：

Collecting sample(s)
from participants：

标本中文名：

结直肠肿瘤标本

组织：

Sample Name：

Colorectal tumor specimen

Tissue：

人体标本去向

 使用后销毁  

说明

Fate of sample：

Destruction after use  

Note：

征募研究对象情况：

Recruiting status：

尚未开始

Not yet recruiting

年龄范围：

Participant age：
最小 Min age 岁 years
最大 Max age 岁 years

性别：

男女均可

Gender：

Both

随机方法（请说明由何人用什么方法产生随机序列）：

无

Randomization Procedure (please state who generates the random number sequence and by what method)：

None

是否公开试验完成后的统计结果:

Calculated Results after the Study Completed public access:

不公开/Private

盲法：

无

Blinding：

None

是否共享原始数据：

IPD sharing

否No

共享原始数据的方式（说明：请填入公开原始数据日期和方式，如采用网络平台，需填该网络平台名称和网址）：

无

The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url)：

None

数据采集和管理（说明：数据采集和管理由两部分组成，一为病例记录表(Case Record Form, CRF)，二为电子采集和管理系统(Electronic Data Capture, EDC)，如ResMan即为一种基于互联网的EDC：

EDC

Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture：

EDC

数据与安全监察委员会：

Data and Safety Monitoring Committee：

有/Yes

注册人：

Name of Registration：

  2026-06-10 09:54:26